## Some pair programming benefits may be mathematical artefacts

Many claims are made about the advantages of pair programming. The claim that the performance of pairs is better than the performance of individuals may actually be the result of the mathematical consequences of two people working together, rather than working independently (at least for some tasks).

Let’s say that individuals have to find a fault in code, and then fix it. Some people will find the fault and then its fix much more quickly than others. The data for the following analysis comes from the report Experimental results on software debugging (late Rome period), via Lutz Prechelt and shows the density of the time taken by each developer to find and fix a fault in a short Fortran program.

Fixing faults is different from many other development tasks in that if often requires a specific insight to spot the mistake; once found, the fixing task tends to be trivial.

The mean time taken, for task t1, is 22.2 minutes (standard deviation 13).

How long might pairs of developers have taken to solve the same problem. We can take the existing data, create pairs, and estimate (based on individual developer time) how long the pair might take (code+data).

Averaging over every pair of 17 individuals would take too much compute time, so I used bootstrapping. Assuming the time taken by a pair was the shortest time taken by the two of them, when working individually, sampling without replacement produces a mean of 14.9 minutes (sd 1.4) (sampling with replacement is complicated…).

By switching to pairs we appear to have reduced the average time taken by 30%. However, the apparent saving is nothing more than the mathematical consequence of removing larger values from the sample.

The larger the variability of individuals, the larger the apparent saving from working in pairs.

When working as a pair, there will be some communication overhead (unless one is much faster and ignores the other developer), so the saving will be slightly less.

If the performance of a pair was the mean of their individual times, then pairing would not change the mean performance, compared to working alone. The performance of a pair has to be less than the mean of the performance of the two individuals, for pairs to show an improved performance.

There is an analytic solution for the distribution of the minimum of two values drawn from the same distribution. If is a probability density function and the corresponding cumulative distribution function, then the corresponding functions for the minimum of a pair of values drawn from this distribution is given by: and .

The presence of two peaks in the above plot means the data is not going to be described by a single distribution. So, the above formula look interesting but are not useful (in this case).

When pairs of values are drawn from a Normal distribution, a rough calculation suggests that the mean is shifted down by approximately half the standard deviation.

## Christmas books for 2018

The following are the really interesting books I read this year (only one of which was actually published in 2018, everything has to work its way through several piles). The list is short because I did not read many books and/or there is lots of nonsense out there.

The English and their history by Robert Tombs. A hefty paperback, at nearly 1,000 pages, it has been the book I read on train journeys, for most of this year. Full of insights, along with dull sections, a narrative that explains lots of goings-on in a straight-forward manner. I still have a few hundred pages left to go.

The mind is flat by Nick Chater. We experience the world through a few low bandwidth serial links and the brain stitches things together to make it appear that our cognitive hardware/software is a lot more sophisticated. Chater’s background is in cognitive psychology (these days he’s an academic more connected with the business world) and describes the experimental evidence to back up his “mind is flat” model. I found that some of the analogues dragged on too long.

In the readable social learning and evolution category there is: Darwin’s unfinished symphony by Leland and The secret of our success by Henrich. Flipping through them now, I cannot decide which is best. Read the reviews and pick one.

Group problem solving by Laughin. Eye opening. A slim volume, packed with data and analysis.

The Digital Flood: The Diffusion of Information Technology Across the U.S., Europe, and Asia by Cortada. Something of a specialist topic, but if you are into the diffusion of technology, this is surely the definitive book on the diffusion of software systems (covers mostly hardware).

## Is it worth attending an academic conference or workshop?

If you work in industry, is it worth attending an academic conference or workshop?

The following observations are based on my attending around 50 software engineering and compiler related conferences/workshops, plus discussion with a few other people from industry who have attended such events.

Slightly longer answer: Perhaps, if you are looking to hire somebody knowledgeable in a particular domain.

Much longer answer: Academics go to conferences to network. They are looking for future collaborators, funding, jobs, and general gossip. What is the point of talking to somebody from industry? Academics will make small talk and be generally friendly, but they don’t know how to interact, at the professional level, with people from industry.

Why are academics generally hopeless at interacting, at the professional level, with people from industry?

Part of the problem is lack of practice, many academic researchers live in a world that rarely intersects with people from industry.

Impostor syndrome is another. I have noticed that academics often think that people in industry have a much better understanding of the realities of their field. Those who have had more contact with people from industry might have noticed that impostor syndrome is not limited to academia.

Talking of impostor syndrome, and feeling of being a fraud, academics don’t seem to know how to handle direct criticism. Again I think it is a matter of practice. Industry does not operate according to: I won’t laugh at your idea, if you don’t laugh at mine, which means people within industry are practiced at ‘robust’ discussion (this does not mean they like it, and being good at handling such discussions smooths the path into management).

At the other end of the impostor spectrum, some academics really do regard people working in industry as simpletons. I regularly have academics express surprise that somebody in industry, i.e., me, knows about this-that-or-the-other. My standard reply is to say that its because I paid more for my degree and did not have the usual labotomy before graduating. Not a reply guaranteed to improve industry/academic relations, but I enjoy the look on their faces (and I don’t expect they express that opinion again to anyone else from industry).

The other reason why I don’t recommend attending academic conferences/workshops, is that lots of background knowledge is needed to understand what is being said. There is no point attending ‘cold’, you will not understand what is being presented (academic presentations tend to be much better organized than those given by people in industry, so don’t blame the speaker). Lots of reading is required. The point of attending is to talk to people, which means knowing something about the current state of research in their area of interest. Attending simply to learn something about a new topic is a very poor use of time (unless the purpose is to burnish your c.v.).

Why do I continue to attend conferences/workshops?

If a conference/workshop looks like it will be attended by people who I will find interesting, and it’s not too much hassle to attend, then I’m willing to go in search of gold nuggets. One gold nugget per day is a good return on investment.

## Practical ecosystem books for software engineers

So you have read my (draft) book on evidence-based software engineering and want to learn more about ecosystems. What books do I suggest?

Biologists have been studying ecosystems for a long time, and more recently social scientists have been investigating cultural ecosystems. Many of the books written in these fields are oriented towards solving differential equations and are rather subject specific.

The study of software ecosystems has been something of a niche topic for a long time. Problems for researchers have included gaining access to ecosystems and the seeming proliferation of distinct ecosystems. The state of ecosystem research in software engineering is rudimentary; historians are starting to piece together what has happened.

Most software ecosystems are not even close to being in what might be considered a steady state. Eventually most software will be really old, and this will be considered normal (“Shock Of The Old: Technology and Global History since 1900″ by Edgerton; newness is a marketing ploy to get people to buy stuff). In the meantime, I have concentrated on the study of ecosystems in a state of change.

Understanding ecosystems is about understanding how the interaction of participant’s motivation, evolves the environment in which they operate.

“Modern Principles of Economics” by Cowen and Tabarrok, is a very readable introduction to economics. Economics might be thought of as a study of the consequences of optimizing the motivation of maximizing return on investment. “Principles of Corporate Finance” by Brealey and Myers, focuses on the topic in its title.

“The Control Revolution: Technological and Economic Origins of the Information Society” by Beniger: the ecosystems in which software ecosystems coexist and their motivations.

“Evolutionary dynamics: exploring the equations of life” by Nowak, is a readable mathematical introduction to the subject given in the title.

“Mathematical Models of Social Evolution: A Guide for the Perplexed” by McElreath and Boyd, is another readable mathematical introduction, but focusing on social evolution.

“Social Learning: An Introduction to Mechanisms, Methods, and Models” by Hoppitt and Laland: developers learn from each other and from their own experience. What are the trade-offs for the viability of an ecosystem that preferentially contains people with specific ways of learning?

“Robustness and evolvability in living systems” by Wagner, survival analysis of systems built from components (DNA in this case). Rather specialised.

Books with a connection to technology ecosystems.

“Increasing returns and path dependence in the economy” by Arthur, is now a classic, containing all the basic ideas.

“The red queen among organizations” by Barnett, includes a chapter on computer manufacturers (has promised me data, but busy right now).

“Information Foraging Theory: Adaptive Interaction with Information” by Pirolli, is an application of ecosystem know-how, i.e., how best to find information within a given environment. Rather specialised.

“How Buildings Learn: What Happens After They’re Built” by Brand, yes building are changed just like software and the changes are just as messy and expensive.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

## Practical psychology books for software engineers

So you have read my (draft) book on evidence-based software engineering and want to learn more about human psychology. What books do I suggest?

I wrote a book about C that attempted to use results from cognitive psychology to understand developer characteristics. This work dates from around 2000, and some of my book choices may have been different, had I studied the subject 10 years later. Another consequence is that this list is very weak on social psychology.

I own all the following books, but it may have been a few years since I last took them off the shelf.

There are two very good books providing a broad introduction: “Cognitive psychology and its implications” by Anderson, and “Cognitive psychology: A student’s handbook” by Eysenck and Keane. They have both been through many editions, and buying a copy that is a few editions earlier than current, saves money for little loss of content.

“Engineering psychology and human performance” by Wickens and Hollands, is a general introduction oriented towards stuff that engineering requires people to do.

Brain functioning: “Reading in the brain” by Dehaene (a bit harder going than “The number sense”). For those who want to get down among the neurons “Biological psychology” by Kalat.

Consciouness: This issue always comes up, so let’s kill it here and now: “The illusion of conscious will” by Wegner, and “The mind is flat” by Chater.

Decision making: What is the difference between decision making and reasoning? In psychology those with a practical orientation study decision making, while those into mathematical logic study reasoning. “Rational choice in an uncertain world” by Hastie and Dawes, is a general introduction; “The adaptive decision maker” by Payne, Bettman and Johnson, is a readable discussion of decision making models. “Judgment under Uncertainty: Heuristics and Biases” by Kahneman, Slovic and Tversky, is a famous collection of papers that kick started the field at the start of the 1980s.

Evolutionary psychology: “Human evolutionary psychology” by Barrett, Dunbar and Lycett. How did we get to be the way we are? Watch out for the hand waving (bones can be dug up for study, but not the software of our mind), but it weaves a coherent’ish story. If you want to go deeper, “The Adapted Mind: Evolutionary Psychology and the Generation of Culture” by Barkow, Tooby and Cosmides, is a collection of papers that took the world by storm at the start of the 1990s.

Language: “The psychology of language” by Harley, is the book to read on psycholinguistics; it is engrossing (although I have not read the latest edition).

Memory: I have almost a dozen books discussing memory. What these say is that there are a collection of memory systems having various characteristics; which is what the chapters in the general coverage books say.

Modeling: So you want to model the human brain. ACT-R is the market leader in general cognitive modeling. “Bayesian cognitive modeling” by Lee and Wagenmakers, is a good introduction for those who prefer a more abstract approach (“Computational modeling of cognition” by Farrell and Lewandowsky, is a big disappointment {they have written some great papers} and best avoided).

Reasoning: The study of reasoning is something of a backwater in psychology. Early experiments showed that people did not reason according to the rules of mathematical logic, and this was treated as a serious fault (whose fault it was, shifted around). Eventually most researchers realised that the purpose of reasoning was to aid survival and reproduction, not following the recently (100 years or so) invented rules of mathematical logic (a few die-hards continue to cling to the belief that human reasoning has a strong connection to mathematical logic, e.g., Evans and Johnson-Laird; I have nearly all their books, but have not inflicted them on the local charity shop yet). Gigerenzer has written several good books: “Adaptive thinking: Rationality in the real world” is a readable introduction, also “Simple heuristics that make us smart”.

Social psychology: “Social learning” by Hoppitt and Laland, analyzes the advantages and disadvantages of social learning; “The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter” by Henrich, is a more populist book (by a leader in the field).

Vision: “Visual intelligence” by Hoffman is a readable introduction to how we go about interpreting the photons entering our eyes, while “Graph design for the eye and mind” by Kosslyn is a rule based guide to visual presentation. “Vision science: Photons to phenomenology” by Palmer, for those who are really keen.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

## Practical statistics books for software engineers

So you have read my (draft) book on evidence-based software engineering and want to learn more about the statistical techniques used, but are not interested lots of detailed mathematics. What books do I suggest?

All the following books are sitting on the shelf next to where I write (not that they get read that much these days).

Before I took the training wheels off my R usage, my general go to book was (I still look at it from time to time): “The R Book” by Crawley, second edition; “R in Action” by Kabacoff is a good general read.

In alphabetical subject order:

Categorical data: “Categorical Data Analysis” by Agresti, the third edition is a weighty tomb (in content and heaviness). Plenty of maths+example; more of a reference.

Compositional data: “Analyzing compositional data with R” by van den Boogaart and Tolosana-Delgado, is more or less the only book of its kind. Thankfully, it is quite good.

Count data: “Modeling count data” by Hilbe, may be more than you want to know about count data. Readable.

Circular data: “Circular statistics in R” by Pewsey, Neuhauser and Ruxton, is the only non-pure theory book available. The material seems to be there, but is brief.

Experiments: “Design and analysis of experiments” by Montgomery.

General: “Applied linear statistical models” by Kutner, Nachtsheim, Neter and Li, covers a wide range of topics (including experiments) using a basic level of mathematics.

Mixed-effects models: “Mixed-effects models in S and S-plus” by Pinheiro and Bates, is probably the book I prefer; “Mixed effects models and extensions in ecology with R” by Zuur, Ieno, Walker, Saveliev and Smith, is another view on an involved topic (plus lots of ecological examples).

Modeling: “Statistical rethinking” by McElreath, is full of interesting modeling ideas, using R and Stan. I wish I had some data to try out some of these ideas.

Regression analysis: “Applied Regression Analysis and Generalized Linear Models” by Fox, now in its third edition (I also have the second edition). I found this the most useful book, of those available, for a more detailed discussion of regression analysis. Some people like “Regression modeling strategies” by Harrell, but this does not appeal to me.

Survival analysis: “Introducing survival and event history analysis” by Mills, is a readable introduction covering everything; “Survival analysis” by Kleinbaum and Klein, is full of insights but more of a book to dip into.

Time series: The two ok books are: “Time series analysis and its application: with R examples” by Shumway and Stoffler, contains more theory, while “Time series analysis: with applications in R” by Cryer and Chan, contains more R code.

There are lots of other R/statistics books on my shelves (just found out I have 31 of Springer’s R books), some ok, some not so. I have a few ‘programming in R’ style books; if you are a software developer, R the language is trivial to learn (its library is another matter).

Suggestions for books covering topics I have missed welcome, or your own preferences (as a software developer).

## The first commercially available (claimed) verified compiler

Yesterday, I read a paper containing a new claim by some of those involved with CompCert (yes, they of soap powder advertising fame): “CompCert is the first commercially available optimizing compiler that is formally verified, using machine assisted mathematical proofs, to be exempt from miscompilation”.

First commercially available; really? Surely there are earlier claims of verified compilers being commercial availability. Note, I’m saying claims; bits of the CompCert compiler have involved mathematical proofs (i.e., code generation), so I’m considering earlier claims having at least the level of intellectual honesty used in some CompCert papers (a very low bar).

What does commercially available mean? The CompCert system is open source (but is not free software), so I guess it’s commercially available via free downloading licensing from AbsInt (the paper does not define the term).

Computational Logic, Inc is the name that springs to mind, when thinking of commercial and formal verification. They were active from 1983 to 1997, and published some very interesting technical reports about their work (sadly there are gaps in the archive). One project was A Mechanically Verified Code Generator (in 1989) and their Gypsy system (a Pascal-like language+IDE) provided an environment for doing proofs of programs (I cannot find any reports online). Piton was a high-level assembler and there was a mechanically verified implementation (in 1988).

There is the Danish work on the formal specification of the code generators for their Ada compiler (while there was a formal specification of the Ada semantics in VDM, code generators tend to be much simpler beasts, i.e., a lot less work is needed in formal verification). The paper I have is: “Retargeting and rehosting the DDC Ada compiler system: A case study – the Honeywell DPS 6″ by Clemmensen, from 1986 (cannot find an online copy). This Ada compiler was used by various hardware manufacturers, so it was definitely commercially available for (lots of) money.

Are then there any earlier verified compilers with a commercial connection? There is A PRACTICAL FORMAL SEMANTIC DEFINITION AND VERIFICATION SYSTEM FOR TYPED LISP, from 1976, which has “… has proved a number of interesting, non-trivial theorems including the total correctness of an algorithm which sorts by successive merging, the total correctness of the McCarthy-Painter compiler for expressions, …” (which sounds like a code generator, or part of one, to me).

Francis Morris’s thesis, from 1972, proves the correctness of compilers for three languages (each language contained a single feature) and discusses how these features may be combined into a more “realistic” language. No mention of commercial availability, but I cannot see the demand being that great.

The definition of PL/1 was written in VDM, a formal language. PL/1 is a huge language and there were lots of subsets. Were there any claims of formal verification of a subset compiler for PL/1? I have had little contact with the PL/1 world, so am not in a good position to know. Anybody?

Over to you dear reader. Are there any earlier claims of verified compilers and commercial availability?

## Publishing information on project progress: will it impact delivery?

Numbers for delivery date and cost estimates, for a software project, depend on who you ask (the same is probably true for other kinds of projects). The people actually doing the work are likely to have the most accurate information, but their estimates can still be wildly optimistic. The managers of the people doing the work have to plan (i.e., make worst/best case estimates) and deal with people outside the team (i.e., sell the project to those paying for it); planning requires knowledge of where things are and where they need to be, while selling requires being flexible with numbers.

A few weeks ago I was at a hackathon organized by the people behind the Project Data and Analytics meetup. The organizers (Martin Paver & co.) had obtained some very interesting project related data sets. I worked on the Australian ICT dashboard data.

The Australian ICT dashboard data was courtesy of the Queensland state government, which has a publicly available dashboard listing digital project expenditure; the Victorian state government also has a dashboard listing ICT expenditure. James Smith has been collecting this data on a monthly basis.

What information might meaningfully be extracted from monthly estimates of project delivery dates and costs?

If you were running one of these projects, and had to provide monthly figures, what strategy would you use to select the numbers? Obviously keep quiet about internal changes for as long as possible (today’s reduction can be used to offset a later increase, or vice versa). If the client requests changes which impact date/cost, then obviously update the numbers immediately; the answer to the question about why the numbers changed is that, “we are responding to client requests” (i.e., we would otherwise still be on track to meet the original end-points).

What is the intended purpose of publishing this information? Is it simply a case of the public getting fed up with overruns, with publishing monthly numbers is seen as a solution?

What impact could monthly publication have? Will clients think twice before requesting an enhancement, fearing public push back? Will companies doing the work make more reliable estimates, or work harder?

Project delivery dates/costs change because new functionality/work-to-do is discovered, because the appropriate staff could not be hired and other assorted unknown knowns and unknowns.

Who is looking at this data (apart from half a dozen people at a hackathon on the other side of the world)?

Data on specific projects can only be interpreted in the context of that project. There is some interesting research to be done on the impact of public availability on client and vendor reporting behavior.

Will publication have an impact on performance? One way to get some idea is to run an A/B experiment. Some projects have their data made public, others don’t. Wait a few years, and compare project performance for the two publication regimes.

## Statistical techniques not needed to analyze software engineering data

One of the methods I used to try to work out what statistical techniques were likely to be useful to software developers, was to try to apply techniques that were useful in other areas. Of course, applying techniques requires the appropriate data to apply them to.

Extreme value statistics are used to spot patterns in rare events, e.g., frequency of rivers over spilling their banks and causing extensive flooding. I have tried and failed to find any data where Extreme value theory might be applicable. There probably is some such data, somewhere.

The fact that I have spent a lot of time looking for data and failed to find particular kinds of data, suggests that occurrences are rare. If data needing a particular kind of analysis technique is rare, there is no point including a discussion of the technique in a book aimed at providing general coverage of material.

I have spent some time looking for data drawn from a zero-inflated Poisson distribution. Readers are unlikely to have ever heard of this and might well ask why I would be interested in such an obscure distribution. Well, zero-truncated Poisson distributions crop up regularly (the Poisson distribution applies to count data that starts at zero, when count data starts at one the zeroes are said to be truncated and the Poisson distribution has to be offset to adjust for this). There is a certain symmetry to zero-truncated/inflated (although the mathematics involved is completely different), plus there is probably a sunk cost effect (i.e., I have spent time learning about them, I am going to find the data).

I spotted a plot in a paper investigating record data structure usage in Racket, that looked like it might be well fitted by a zero-inflated Poisson distribution. Tobias Pape kindly sent me the data (number of record data structures having a given size), which I then failed miserably to fit to any kind of Poisson related distribution; see plot below; data points along red line through the plus symbols (code+data):

I can only imagine what the authors thought of my reason for wanting the data (I made data requests to a few other researchers for similar reasons; and again I failed to fit the desired distribution).

I had expected to make more use of time series analysis; but, it has just not been that applicable.

Machine learning is useful for publishing papers, but understanding what is going on is the subject of my book, not building black boxes to make predictions.

It is possible that researchers are not publishing work relating to data that requires statistical techniques I have not used, because they don’t know how to analyze the data or the data is too hard to collect. Inability to use the correct techniques to analyze data is rarely a reason for not publishing a paper. Data being too hard to collect is very believable, as-is the data rarely occurring in software engineering related work.

There are statistical tests I have intentionally ignored, the Mann–Whitney U test (aka, the Wilcoxon rank-sum test) and the t-test probably being the most well-known. These tests became obsolete once computers became generally available. If you are ever stuck on a desert island without a computer, these are the statistical tests you will have to use.

## Students vs. professionals in software engineering experiments

Experiments are an essential component of any engineering discipline. When the experiments involve people, as subjects in the experiment, it is crucial that the subjects are representative of the population of interest.

Academic researchers have easy access to students, but find it difficult to recruit professional developers, as subjects.

If the intent is to generalize the results of an experiment to the population of students, then using student as subjects sounds reasonable.

If the intent is to generalize the results of an experiment to the population of professional software developers, then using student as subjects is questionable.

What it is about students that makes them likely to be very poor subjects, to use in experiments designed to learn about the behavior and performance of professional software developers?

The difference between students and professionals is practice and experience. Professionals have spent many thousands of hours writing code, attending meetings discussing the development of software; they have many more experiences of the activities that occur during software development.

The hours of practice reading and writing code gives professional developers a fluency that enables them to concentrate on the problem being solved, not on technical coding details. Yes, there are students who have this level of fluency, but most have not spent the many hours of practice needed to achieve it.

Experience gives professional developers insight into what is unlikely to work and what may work. Without experience students have no way of evaluating the first idea that pops into their head, or a situation presented to them in an experiment.

People working in industry are well aware of the difference between students and professional developers. Every year a fresh batch of graduates start work in industry. The difference between a new graduate and one with a few years experience is apparent for all to see. And no, Masters and PhD students are often not much better and in some cases worse (their prolonged sojourn in academia means that have had more opportunity to pick up impractical habits).

It’s no wonder that people in industry laugh when they hear about the results from experiments based on student subjects.

Just because somebody has “software development” in their job title does not automatically make they an appropriate subject for an experiment targeting professional developers. There are plenty of managers with people skills and minimal technical skills (sub-student level in some cases)

In the software related experiments I have run, subjects were asked how many lines of code they had read/written. The low values started at 25,000 lines. The intent was for the results of the experiments to be generalized to the population of people who regularly wrote code.

Psychology journals are filled with experimental papers that used students as subjects. The intent is to generalize the results to the general population. It has been argued that students are not representative of the general population in that they have spent more time reading, writing and reasoning than most people. These subjects have been labeled as WEIRD.

I spend a lot of time reading software engineering papers. If a paper involves human subjects, the first thing I do is find out whether the subjects were students (usual) or professional developers (not common). Authors sometimes put effort into dressing up their student subjects as having professional experience (perhaps some of them have spent a year or two in industry, but talking to the authors often reveals that the professional experience was tutoring other students), others say almost nothing about the identity of the subjects. Papers describing experiments using professional developers, trumpet this fact in the abstract and throughout the paper.

I usually delete any paper using student subjects, some of the better ones are kept in a subdirectory called `students`.

Software engineering researchers are currently going through another bout of hand wringing over the use of student subjects. One paper makes the point that a student based experiment is a good way of validating an experiment that will later involve professional developers. This is a good point, but ignored the problem that researchers rarely move on to using professional subjects; many researchers only ever intend to run student-based experiments. Also, they publish the results from the student based experiment, which are at best misleading (but academics get credit for publishing papers, not for the content of the papers).

Researchers are complaining that reviews are rejecting their papers on student based experiments. I’m pleased to hear that reviewers are rejecting these papers.

## The best or most compiler writers born in February?

Some years ago, now, I ran a poll asking about readers’ month of birth and whether they had worked on a compiler. One hypothesis was that the best compiler writers are born in February, an alternative hypothesis is that most compiler writers are born in February.

I have finally gotten around to analyzing the data and below is the Rose diagram for the 82, out of 132 responses, compiler writers (the green arrow shows the direction and magnitude of the mean; code+data):

At 15% of responses, February is the most common month for compiler writer birthdays. The percentage increases to 16%, if weighted by the number of births in each month.

So there you have it, the hypothesis that most compiler writers are born in February is rejected, leaving the hypothesis that the best compiler writers are born in February. How could this not be true

What about the birth month of readers who are not compiler writers? While the mean direction and length are more-or-less the same, for the two populations, the Rose diagram shows that the shape of the distributions are different:

I was at Facebook’s first Big Code Summit on Monday and Tuesday (I say the first, because I hope there is another one next year).

The talks all involved machine learning (to be expected, given the Big Code in the event’s title). Normally I ignore papers on machine learning in software engineering, but understanding code is hard and we don’t know much about it. As I keep telling anyone who will listen, machine learning is the tool to use when you don’t know what you are doing (provided you have enough data).

People have been learning code patterns for some time now, suggesting applications in code completion in the IDE and finding suspicious API sequences (e.g., a missing call). This is one area where machine learning is a natural solution: nobody has the time to write down all the common patterns, for all the common languages, and APIs are constantly changing. It makes no sense to solve this problem manually.

So what was new and/or interesting?

We got new and very interesting in the first talk, when Eran Yahav presented his group’s work on cod2vec, the paper was interesting, but the demo had the wow factor.

I have not made up my mind about Michael Pradel‘s proposal for learning new coding rule checks. These rules are often created by people, but people with the necessary skill are thin on the ground. Machine learning requires something to learn from, how could coding rules be created this way. Michael’s group is working on a system where developers create the positive and negative cases and a machine learner figures out rules from these examples. Would the creation of these positive/negative examples prove to be just as hard as writing rules? I was not convinced that such an approach was practical, but if somebody wants to try it out, why not.

I found Xinyun Chen‘s talk interesting, but then I’ve written lots of parsers, and automatically figuring out how to parse a language from examples will always get my attention. A few people in the audience thought that a better solution was typing in a grammar and parsing the ‘usual’ way. This approach assumes a grammar exists, can be strong-armed into a form that is practical to embed in a parser (requiring somebody skilled in the necessary black arts), to produce a system that will only process complete translation units (or whatever the language calls a unit of translation).

## Adding a new scalar type to C

I think the time has arrived for a new scalar type in C, which for want of a better name I shall call the `compendium` type.

On today’s processors a compendium type behaves a lot like an integer type, except that nobody really wants to include it in the list of supported integer types, e.g., 128-bit scalars.

Why is a new scalar type needed? The Standard supports extended integer types, why not treat a scalar object that supports integer arithmetic as an integer type?

The C Standard says (section 6.2.5 Types):
“There are five standard signed integer types, designated as `signed char`, `short int`, `int`, `long int`, and `long long int`. (These and other types may be designated in several additional ways, as described in 6.7.2.) There may also be implementation-defined extended signed integer types.38) The standard and extended signed integer types are collectively called signed integer types.39)”

There is corresponding wording for unsigned integer types.

The standard header allows implementations to define a whole menagerie of integer types: section 7.20.1.1 Exact-width integer types
“The typedef name `intN_t` designates a signed integer type with width N, no padding bits, and a two’s complement representation. Thus, `int8_t` denotes such a signed integer type with a width of exactly 8 bits.”

This all sounds very feasible, but there is a catch. The Standard defines a greatest-width integer type, section 7.20.1.5 Greatest-width integer types
“The following type designates a signed integer type capable of representing any value of any signed integer type:
`intmax_t`

and various library functions have an argument type `intmax_t` (there is also an `uintmax_t`).

An ‘extra-large’ integer type is not something that can just sit there, in the list of available integer types, waiting to be used. Preprocessor arithmetic and a variety of library are based around the type `intmax_t`. An extra-large integer type would have a very visible impact on all developers, many of whom would want to ignore it.

GCC supports 128-bit integers, e.g., `__int128`. But some magic pixie dust is involved, this type has no connection with `intmax_t`.

What do developers do with these 128- and 256-bit scalar objects? Evaluating graphics algorithms, hashes and cryptographic calculations are obvious candidates; yes, perhaps even calculations involving integers that require this many bits. I have not seen any analysis of the uses of this kind of wide-integer-like type.

Extra-wide scalar types have a variety of uses and the term `compendium` type, captures this. Hardware support for such extra-width types is growing, with vendors looking to fill major niches.

Contorting existing wording, in the Standard, so accommodate these extra-wide types within the existing integer type machinery is a short term solution. Work on the upcoming revision of the C Standard should either do nothing and allow vendors to take the approach currently used by GCC, or create a new scalar type (perhaps using a TR).

## The Nostradamus argument in software engineering research

The Nostradamus argument in software engineering research goes something like: This idea was proposed in a paper by XX, some years ago.

I regularly encounter the Nostradamus argument when discussing what people in industry are doing, with one or more academics. The same argument is probably made in other fields.

The rules of academic research pretty much guarantee that somebody, at sometime, has published a paper containing an idea related to something being discussed today.

The first researcher(s) to publish an idea gets the credit for the idea, and ‘uses it up’ the idea, that is somebody else cannot subsequently publish a paper claiming that idea (it does happen, either through plagiarism or slip-ups during review).

The job of researchers is to find new ideas (well, actually these days it is to quickly find an idea that will get published; researchers are on a publication treadmill). Sometimes a paper will explicitly point out the novel idea they are claiming (usually a sign of a very poor paper; the author(s) obviously don’t feel confident that the reader will see anything of merit). Researchers also talk of gaps in the literature, i.e., some topic where little, if anything, has been published.

Before starting work in an area, researchers are supposed to read all relevant prior publications; this can be an awful lot of work and take a lot of time. In practice people tend to read the papers in the top 10, or so, journals published in the last few years; maybe looking at more journals and going further back in time if the initial search fails to return many results. I have had many conversations with researchers about a paper, or thesis, they are just completing and been told “I’m just finishing off the literature search”, i.e., they are doing the background checks after completing their research, not before (yes, sometimes rather similar work has already been published and some quick footwork is needed).

So the work of prior researchers is venerated in theory, but rarely in practice.

## The world view of research in software engineering

For a long time I have been trying to figure out why so much research in software engineering is so obviously unconnected to the reality of software development.

As might have been guessed, the answer has been staring me in the face for some time.

Many researchers in software engineering have a modified mathematicians’ world view of research, i.e., investigate things we find interesting (the mathematicians’ view) and some years from now industry will discover our work and apply it (the modification). I have had multiple academics essentially say this to me and I had not appreciated that I need to argue against a world view (not specific points of that view). This mathematician world view also explains why my questions about evidence receive such baffled looks; and, I am regularly told that experiments cannot be done, or are meaningless, in software engineering research.

Which research field’s world view might be closest to software engineering? I would nominate drug discovery.

Claims made by researchers in drug discovery are expected to be backed up with evidence. There are problems to be solved (e.g., diseases to be cured) and researchers try out ideas by running experiments. They don’t put lots of time and effort into creating a new drug, propose this drug as cure for some disease and then wait for industry to run some experiments, to see if the claims are true. I’m a regular reader of In The Pipeline, an interesting drug discover blog that is accessible to those outside the field.

How do I argue against a world view? I have no idea; even if I did, I am not looking to start a crusade.

At least I now have a model of the situation that makes sense. Next month, I will be attending some workshops where there will be lots of researchers and I will get to try out my new insight.

## A 1948 viewpoint on developer vs. computer time

For a long time now developer time has been a lot more expensive than computer time. The idea that developers should organize what they do, so as to maximize the efficiency of computer time rather than their own time, is considered to be an echo from a bygone age.

Until recently, I thought the transition from this bygone age, when computer time was considered more important than developer time, started in the late 1960s. Don’t ask me why I thought this, put it down to personal bias.

I was recently reading A Survey of Eniac Operations and Problems: 1946-1952, published in 1952, and what did I find:

“Early in 1948, R. F. Clippinger and some of his associates, in the course of coding the solution of …, were forced to adopt a different method of using the Eniac in order to fit their problem on the machine. …. The experience with this method (first discussed in reference 1), led J. von Neumann to suggest the use of a serial code for control of the Eniac. Such a code was devised and employed with the Eniac beginning in March 1948. Operation of the Eniac with this code was several times slower than either the original method of direct programming or the code for parallel operation. However, the resulting simplification of coding techniques and other advantages far outweighed this disadvantage.

In other words, in 1948, the people using one of the few computers in the world, which clocked at 100KHz, considered developer time to be more important than computer time.

## Major players in evidence-based software engineering

Who are the major players in evidence-based software engineering?

How might ‘majorness’ of players be calculated? For me, the amount of interesting software engineering data they have made publicly available is the crucial factor. Any data published in a book, paper or report is enough to be considered interesting. How interesting is data published on a web page? This is a tough question, let’s dodge the question to start with, and consider the decades before the start of 2000.

In the academic world performance is based on number of papers published, the impact factor of where they were published and number of citations of those papers. This skews the results in favor of those with lots of students (who tack their advisor’s name on the end of papers published) and those who are good at marketing.

Historians of computing have primarily focused on the evolution of hardware and are slowly moving to discuss software (perhaps because microcomputers have wiped out nearly every hardware vendor). So we will have to wait perhaps a decade or two for tentative/definitive historian answer.

The 1950s

Computers and Automation is a criminally underused resource (a couple of PhDs worth of primary data here). A lot of the data is hardware related, but software gets a lot more than a passing mention.

The US military published lots of hardware data, but software does not get mentioned much.

The 1960s

Computers and Automation are still publishing.

The US military still publishing data; again mostly hardware related.

Datamation, a weekly news magazine, published a lot of substantial material on the software and hardware ecosystems as they evolved.

Kenneth Knight’s analysis of computer performance is an example of the kind of data analysis that many people undertook for hardware, which was rarely done for software.

The 1970s

The US military are still leading the way; we are in the time of Rome. Air Force officers studying for a Master’s degree publish more software engineering data than all academics combined over this and the next two decades.

“Data processing technology and economics” by Montgomery Phister is 720 A4 pages packed with graphs and tables of numbers. Despite citing earlier sources, this has become the primary source for a lot of subsequent researchers; this is understandable in a pre-internet age. Now we have Bitsavers and the Internet Archive, and the cited primary source can be downloaded.

NASA is surprisingly low volume.

The 1980s

Rome falls (i.e., the work gets outsourced to a university) and the false prophets (i.e., academics doing non-evidence based work) multiply and prosper. There are hushed references to trouble makers performing unclean acts experiments in the wilderness.

A few people working in the wilderness, meaning that the quantity of data being produced drops by at least an order of magnitude.

The 1990s

Enough time has passed for people to be able to refer to the wisdom of the ancients.

There are still people in the wilderness howling at the moon, and performing unclean acts experiments.

The 2000s

Repositories of Open source and bug reports grow and prosper. Evidence-based software engineering research starts to become mainstream.

There are now groups of people doing software engineering research.

What about individuals as major players? A vaguely scientific way of rating individual impact, on evidence-based software engineering, is to count the number of papers they have published, that are cited by a book claiming to discuss all the important/interesting publicly available software engineering data (code+data).

The 1,521 papers cited, by such a book, had 3,716 authors, of which 3,095 were different. The authors who appeared most often are listed below (count on the right, and yes, at number 2 is a theoretician; I have cited myself nine times, but two of those are to web sites hosting data).

The number of authors/papers follows the usual pattern of many people writing one paper.

Who might I have missed? The business school researchers don’t get a mention because their data is often covered by a confidentiality agreement. The machine learning crowd are just embarrassing.

Suggestions for major players welcome.

## Business school research in software engineering is some of the best

There is a group of software engineering researchers that don’t feature as often as I would like in my evidence-based software engineering book; academics working in business schools.

Business school academics have written some of the best papers I have read on software engineering; the catch is that the data they use is confidential. For somebody writing a book that only discusses a topic if there is data publicly available, this is a problem.

These business school researchers show that it is possible for academics to obtain ‘interesting’ software engineering data from industry. My experience with talking to researchers in computing departments is that most are too involved in their own algorithmic bubble to want to talk to anybody else.

One big difference between the data analysis papers written by academics in computing departments and business schools, is statistical sophistication. Computing papers are still using stone-age pre-computer age techniques, the business papers use a wide range of sophisticated techniques (sometimes cutting edge).

There is one aspect of software engineering papers written by business school researchers that grates with me, many of the authors obviously don’t understand software engineering from a developer’s perspective; well, obviously, they are business oriented people.

The person who has done the largest amount of interesting software engineering research, whose work I don’t (yet; I will find a way) discuss, is Chris Kemerer; a researcher who has a long list of empirical papers going back to the late 1980s, and rarely gets cited by papers by people in computing departments (I am the only person I know, who limits themself to papers where the data is publicly available).

## Moving to the 12th circle in fault prediction modeling

Most software fault prediction papers are based on a false assumption, i.e., a list of dates when a fault was first experienced, by a program, contains enough information to build a model that has a connection to reality. A count of faults that have been experienced twice is required to fit a basic model that has some mathematical connection to reality.

I had thought that people had moved on from writing papers that fitted yet more complicated equations to one of the regularly used data sets. No, it seems they have just switched to publishing someplace they have not been seen before.

Table 1 lists the every increasing number of circles within circles; the new model is proposed as the 12th refinement (the table is a summary, lots of forks have been proposed over the years). I have this sinking feeling there is another paper in the works, one that ‘benchmarks’ the new equation using a collection of the other regular characters data sets that appear in papers of this kind.

Fitting an equation to data of first experience of a fault is little better than fitting noise.

As Planck famously said, science advances one funeral at a time.

## Grammar checking in 2018

I am a big fan of using tools to find problems quickly. Polishing the draft material for my evidence-based software engineering book, I have been finding an annoying number of grammatical mistakes :-(.

LanguageTool is what I use to check my grammar; it is the best tool of its kind that I know, and supports lots of different languages.

I also have an awk script that looks for new instances of previous mistakes I have made. It rarely flags anything, I seem to be in a continual state of making new grammatical mistakes.

Stung by a recent series is blatant mistakes, I have been searching for a better tool. What did I find:

So, lots of interesting stuff, but nothing better that is usable.

I keep looking at the interesting things that spaCY can do (if you are looking to integrate language processing in your app, spaCY is currently the best language processing library). Does anybody know of grammar checking work being done using spaCY (LanguageTool is based around a parsing engine that is rather long in the tooth now)?

Anybody interested in organizing a grammar checking tool hack day in London?

## Experimental Psychology by Robert S. Woodworth

I have just discovered “Experimental Psychology” by Robert S. Woodworth; first published in 1938, I have a reprinted in Great Britain copy from 1951. The Internet Archive has a copy of the 1954 revised edition; it’s a very useful pdf, but it does not have the atmospheric musty smell of an old book.

The Archives of Psychology was edited by Woodworth and contain reports of what look like ground breaking studies done in the 1930s.

The book is surprisingly modern, in that the topics covered are all of active interest today, in fields related to cognitive psychology. There are lots of experimental results (which always biases me towards really liking a book) and the coverage is extensive.

The history of cognitive psychology, as I understood it until this week, was early researchers asking questions, doing introspection and sometimes running experiments in the late 1800s and early 1900s (e.g., Wundt and Ebbinghaus), behaviorism dominants the field, behaviorism is eviscerated by Chomsky in the 1960s and cognitive psychology as we know it today takes off.

Now I know that lots of interesting and relevant experiments were being done in the 1920s and 1930s.

What is missing from this book? The most obvious omission is equations; lots of data points plotted on graph paper, but no attempt to fit an equation to anything, e.g., an exponential curve to the rate of learning.

A more subtle omission is the world view; digital computers had not been invented yet and Shannon’s information theory was almost 20 years in the future. Researchers tend to be heavily influenced by the tools they use and the zeitgeist. Computers as calculators and information processors could not be used as the basis for models of the human mind; they had not been invented yet.

## Impact of team size on planning, when sitting around a table

A recent blog post by Allan Kelly caught my attention; on Monday Allan sent me some comments on the draft of my book and I got to ask for a copy of his data (you don’t need your own software engineering data before sending me comments).

During an Agile training course he gives, Allan runs an exercise based on an extended version of the XP game. The basic points are: people form into teams, a task is announced, teams have to estimate how long it will take them to complete the task and then to plan the task implementation. Allan recorded information on team size, time spent estimating and time spent planning (no information on the tasks, which were straightforward, e.g., fold a paper airplane).

In a recent post I gave a brief analysis of team size on productivity. What does this XP game data have to say about the impact of team size on performance?

We don’t have task information, but we do have two timing measurements for each team. With a bit of suck-it-and-see analysis, I found that the following equation explained 50% of the variance (code+data):

There was some flexibility in the numbers, depending on the method used to build the regression model.

The introduction of each new team member incurs a fixed overhead. Given that everybody is sitting together around a table, this is not surprising; or, perhaps the problem was so simply that nobody felt the need to give a personal response to everything said by everybody else; or, perhaps the exercise was run just before lunch and people were hungry.

I am not aware of any connection between time spent estimating and time spent planning, but then I know almost nothing about this kind of XP game exercise. That square-root looks interesting (an exponent of 0.4 or 0.6 was a slightly less good fit). Thoughts and experiences anybody?

## 2018 in the programming language standards’ world

I am sitting in the room, at the British Standards Institution, where today’s meeting of IST/5, the committee responsible for programming languages, has just adjourned (it’s close to where I have to be in a few hours).

BSI have downsized us, they no longer provide a committee secretary to take minutes and provide a point of contact. Somebody from a service pool responds (or not) to emails. I did not blink first to our chair’s request for somebody to take the minutes

What interesting things came up?

It transpires that reports of the death of Cobol standards work may be premature. There are a few people working on ‘new’ features, e.g., support for JSON. This work is happening at the ISO level, rather than the national level in the US (where the real work on the Cobol standard used to be done, before being handed on to the ISO). Is this just a couple of people pushing a few pet ideas or will it turn into something more substantial? We will have to wait and see.

The Unicode consortium (a vendor consortium) are continuing to propose new pile of poo emoji and WG20 (an ISO committee) were doing what they can to stay sane.

Work on the Prolog standard, now seems to be concentrated in Austria. Prolog was the language to be associated with, if you were on the 1980s AI bandwagon (and the Japanese were going to take over the world unless we did something about it, e.g., spend money); this time around, it’s machine learning. With one dominant open source implementation and one commercial vendor (cannot think of any others), standards work is a relic of past glories.

In pre-internet times there was an incentive to kill off committees that were past their sell-by date; it cost money to send out mailings and document storage occupied shelf space. In an electronic world there is no incentive to spend time killing off such committees, might as well wait until those involved retire or die.

WG23 (programming language vulnerabilities) reported lots of interest in their work from people involved in the C++ standard, and for some reason the C++ committee people in the room started glancing at me. I was a good boy, and did not mention bored consultants.

It looks like ISO/IEC 23360-1:2006, the ISO version of the Linux Base Standard is going to be updated to reflect LBS 5.0; something that was not certain few years ago.

## Maximum team size before progress begins to stall

On multi-person projects people have to talk to each other, which reduces the amount of time available for directly working on writing software. How many people can be added to a project before the extra communications overhead is such that the total amount of code, per unit time, produced by the team decreases?

A rarely cited paper by Robert Tausworthe provides a simple, but effective analysis.

Activities are split between communicating and producing code.

If we assume the communications overhead is give by: , where is the percentage of one person’s time spent communicating in a two-person team, the number of developers and a constant greater than zero (I’m using Tausworthe’s notation).

The maximum team size, before adding people reduces total output, is given by: .

If (i.e., everybody on the project has the same communications overhead), then , which for small is approximately . For example, if everybody on a team spends 10% of their time communicating with every other team member: .

In this team of five, 50% of each persons time will be spent communicating.

If , then we have .

What if the percentage of time a person spends communicating with other team members has an exponential distribution? That is, they spend most of their time communicating with a few people and very little with the rest; the (normalised) communications overhead is: , where is a constant found by fitting data from the two-person team (before any more people are added to the team).

The maximum team size is now given by: , and if , then: .

In this team of ten, 63% of each persons time will be spent communicating (team size can be bigger, but each member will spend more time communicating compared to the linear overhead case).

Having done this analysis, what is now needed is some data on the distribution of individual communications overhead. Is the distribution linear, square-root, exponential? I am not aware of any such data (there is a chance I have encountered something close and not appreciated its importance).

I have only every worked on relatively small teams, and am inclined towards the distribution of time spent communicating not being constant. Was it exponential or a power-law? I would not like to say.

Could a communications time distribution be reverse engineered from email logs? The cc’ing of people who might have an interest in a topic complicates the data analysis; time spent in meetings are another complication.

Pointers to data most welcome and as is any alternative analysis using data likely to have a higher signal/noise ratio.

## StatsModels: the first nail in R’s coffin

In 2012, when I decided to write a book on evidence-based software engineering, R was the obvious system to use for data analysis. At the time, lots of new books had “using R” or “with R” added at the end of their titles; I chose “using R”.

When developers tell me they need to do some statistical analysis, and ask whether they should use Python or R, I tell them to use Python if statistics is a small part of the program, otherwise use R.

If I started work on the book today, I would till choose R. If I were starting five-years from now, I could be choosing Python.

To understand why I think Python will eventually take over the niche currently occupied by R, we need to understand the unique selling points of both systems.

R’s strengths are that it supports a way of thinking that is a good fit for doing data analysis and has an extensive collection of packages that simplify the task of applying a wide variety of analysis techniques to data.

Python also has packages supporting the commonly used data analysis techniques. But nearly all the Python packages provide a developer-mentality interface (i.e., they provide an API like any other package), R provides data-analysis-mentality interfaces. R supports a way of thinking that data analysts can identify with.

Python’s strengths, over R, are a much larger base of developers and language support for writing large programs (R is really a scripting language). Yes, Python has a package ecosystem supporting the full spectrum of application domains, this is not relevant for analysing a successful invasion of R’s niche market (but it is relevant for enticing new developers who are still making up their mind).

StatsModels is a Python package based around R’s data-analysis-mentality interface. When I discovered this package a few months ago, I realised the first nail had been hammered into R’s coffin.

Yes, today R has nearly all the best statistical analysis packages and a large chunk of the leading edge stuff. But packages can be reimplemented (C code can be copy-pasted, the R code mapped to Python); there is no magic involved. Leading edge has a short shelf life, and what proves to be useful can be duplicated; the market for leading edge code in a mature market (e.g., data analysis) is tiny.

A bunch of bright young academics looking to make a name for themselves will see the major trees in the R forest have been felled. The trees in the Python data-analysis-mentality forest are still standing; all it takes is a few people wanting to be known as the person who implemented the Python package that everybody uses for XYZ analysis.

A collection of packages supporting the commonly (and eventually not so commonly) used data analysis techniques, with a data-analysis-mentality interface, removes a major selling point for using R. Python is a bigger developer market with support for many other application domains.

The flow of developers starting out with R will slow down, casual R users will have nothing to lose from trying out another language when the right project comes along (another language on the CV looks good and Python is a bigger market). There will be groups where everybody uses R and will continue to use R because that is what everybody else in the group uses. Ten-Twenty years from now R, developers could be working in a ghost town.

## What statistical techniques are useful for software engineering data?

What statistical techniques are of general usefulness for analyzing software engineering data?

The answer depends on the kinds of data likely to be encountered, in software engineering, and the questions likely to be asked.

When I started working on a book, aiming to cover all worthwhile publicly available software engineering data, I was hoping to refer readers to a book (or two) that they ought to read to learn the appropriate techniques. Kabacoff’s “R in Action” comes closest to the book I had in mind as a basic introduction, but there was nothing covering a wider range of topics; so I ended up writing something; I found Crawley’s “The R book”, to be the best book on the subject.

My answer to the kinds of data likely to be available was to work with all the software engineering data I could get obtain (around 600 data sets to date).

What questions should be asked about the data? My selection of questions was driven by whether the data was used in the software engineering half of the book, or the statistical analysis techniques half.

The software engineering material consists of the chapters: Introduction, Human cognitive characteristics, Cognitive capitalism, Ecosystems, Projects, Reliability and Source code. The data appeared in one of these chapters if it could be used to make (what I thought was) a practical point about the topic being discussed.

Data appeared in the statistical analysis techniques chapters, if it could be used to illustrate the technique under discussion.

What happened in practice was the software engineering material was worked on for a year or two, on realizing that bespoke statistical analysis material was needed the existing data was plundered to create the necessary chapters; after this was released, work switched back to the software engineering material (using unplundered and newly acquired data), and of course the earlier chapters plundered data from the yet to be worked on chapters.

This seems to have worked surprisingly well, at least from my perspective of keeping the production line going.

Now most if the data has been analyzed, it’s time to take a global overview and where necessary shuffle things around. I may find that everything is a complete mess; we shall see.

What techniques have I found to be useful?

The number 1, most useful data analysis technique is building a regression model. The one thing I have been consistently able to do, when analyzing other people’s data, is extract more information from it than they did (unless they also built a regression model); at times it has been embarrassing.

At number 2, is bootstrapping. Many widely used techniques only give accurate answers if the data has a normal/gaussian distribution and use of these techniques can involve a lot of arm waving involving claims about the data having a good-enough gaussian-like distribution. This arm waving was necessary before computers became available, because the practical manual techniques required a gaussian distribution. Now we have computers and techniques that don’t require any particular distribution can be used, and which in some cases are more powerful techniques than those designed for manual implementation.

Sitting here, I cannot think of a number 3; there might be one.

What techniques are not generally useful? The various tests containing some combination of the names Wilcoxon, Mann and Whitney are well past their sell-by date. Searching the source of the book I see these names still appear in one or two places; this is a hangover from the early versions from many years ago (when I was following the clueless herd) and will soon be gone.

I thought that extreme value theory might apply to some data, but have only found one data-set to which it might be applied (so not generally useful).

I spent a lot of time watching out for zero-inflated data (data containing more zero values than expected by the common probability distributions). I saw four/five papers containing plots of data that looked zero-inflated and emailed the authors asking for the data (who kindly sent it to me). None of the data turned out to be zero-inflated (I’m not sure what the authors thought about being asked for data that somebody thought was zero-inflated). This does not mean that software engineering data is not zero-inflated, only that it is not common.

My zero-inflated search was motivated by the occasional appearance of zero-truncated data (data with that does not contain zero values). Zero-truncated data occurs when counting starts at one, rather than zero (I have one data-set that is 0/1 truncated; the counting starts at 2).

I was surprised that time-series did not turn out to be widely useful.

Sometimes we are all clueless button pushers, so machine learning gets a few pages. Anybody who knows what they are doing builds regression models.

I will eventually get around to counting how many times each technique is used on the data I have (watch this blog, but don’t hold your breath).

## Source code chapter added to “Evidence-based software engineering using R”

The Source Code chapter of my evidence-based software engineering book has been added to the draft pdf (download here).

This chapter has suffered from coming last and there is still lots of work to be done. Almost all the source code related data has been plundered to fill up earlier chapters. Some data did not make the cut-off for release of the draft; a global review will probably result in some data migrating back to this chapter.

When talking to developers about the book I am constantly being asked ‘what is empirical software engineering?’ My explanation uses the phrase ‘evidence-based’, which everybody seems to immediately understand. It is counterproductive having a title that has to be explained, so I have changed the title to “Evidence-based Software Engineering using R”.

What is the purpose of a chapter discussing source code in a book on evidence-based software engineering? Source code is obviously an essential component of the topics discussed in the other chapters, but what is so particular to source code that it could not be said elsewhere? Having spent most of my professional life studying source code, first as a compiler writer and then involved with static analysis, am I just being driven by an attachment to the subject?

My view of source code is very different from most other developers: when developers talk about code, they spend most of the time talking about how they do things, when I talk about code I spend most of the time talking about how other developers do things (I’m a mongrel writer of code). Developers’ blinkered view of code prevents them seeing bigger pictures. I take a Gricean view of code and refrain from using meaningless marketing terms such as maintainability, readability and testability.

I have lots of source code data of interest to compiler writers (who are not the target audience) and I have lots of data related to static analysis (tool developers are not the audience). The target audience is professional software developers and hopefully what has been written is of interest to that readership.

I have been promised all sorts of data. Hopefully some of it will arrive. If somebody tells you they promised to send me data, please encourage them to take some time to sort out the data and send it.

As always, if you know of any interesting software engineering data, please tell me.

Finalizing the statistical analysis material in the second half of the book (released almost two years ago) next.

## Perl’s failure to grow and Python takes over

Perl, once the most widely used scripting language, has been in decline for many years; the decline now looks terminal (many decades from now, when its die-hard users have died), what happened?

Python is what happened. Why was this? Did Perl have a major fail, did Python acquire pixie dust that could not be replicated, or something else?

Some commentators point to the failure to produce a timely release of Perl 6; a major reworking of the language announced in 2000 with a stumbling release made available around 2015.

I think the real issue is a failure for Perl to take off outside its core use as a systems language. Perl is famous for its one-liners, but not for writing large programs (yes, it can be done, but would many developers would really want to?); a glance of the categories in its module library shows; those 174,970 modules (at the time of writing) are not widely spread over application domains (i.e., not catering to a wide audience).

Perl 5 was failing to grow outside its base before Perl 6 began its protracted failure to launch.

Language use is a winner take-all game, developers create more packages, support tools, and new users who combine to attract more developers. Continuing support for minority languages comes from die-hard users, existing software that is worth somebody paying to maintain and niche advantages.

These days, language success is founded on the associated package ecosystem (Go and Rust have minuscule package ecosystems, which is why they are living on borrowed time, other languages will eventually take away their sheen of trendiness). Developers use languages to build stuff, the days of writing the code for almost everything are long gone; interesting software is created by taking advantage of packages written by others. Python was in the right place, at the right time to acquire a wide variety of commercial grade packages.

It’s difficult to see Python being displaced as the lingua franca of software development. Its language features are almost irrelevant, its package ecosystem is everything. The winner will eventually take all.

I’m sure the cycle of languages becoming popular for a few years, before disappearing, will continue. There have always been, and will always be, fashionable languages.

## Evolutionary pressures on C++, Java and Python

The future evolution of C++, Java and Python is being driven by very different interested parties, and it’s going to be interesting watching events unfold over the next 5-10 years.

I have previously written about how the C++ Standard’s committee is past its sell-by date, has taken off its ball and chain and is now in the hands of bored consultants.

Bjarne Stroustrup was once effectively treated as C++’s Benevolent Dictator For Life (during the production of the first C++ Standard some people were labeled as Bjarne groupees); things have moved on since then, but the ‘old-guard’ are trying to make a comeback. Suggesting that people ought to base their thinking on a book published almost 25-years ago (Stroustrup’s “The Design and Evolution of C++”; a very interesting book that is well worth reading) creates a rather backward looking image. Bored consultants are looking to work on exciting new ideas. The old-guard need to appear modern to attract followers (even if the ideas are old ideas with a fresh coat of paint).

The threat to C++ is from bored consultants, each adding their own pet idea to the language standard; a situation that Stroustrup thinks is starting to happen.

Java, the language, is owned by Oracle, the company (let’s not get too involved in exactly what they own, have copyright on, etc). Oracle are not shy about asking people for licensing fees. Java is now on a 6-month release cycle (at least the Oracle version, there are Open Source implementations) and the free support only applies to the current release; paying a license fee buys support for versions older than 6-months. In the short term, the cheapest solution is for companies to pay for support.

Oracle are always happy to send in the lawyers and if too many customers switch to non-Oracle implementations, I’m sure something can be found to introduce enough uncertainty to discourage work/distribution involving Open Source Java implementations.

Will Java survive Oracle’s licensing? It is not in their interest for Java to die; Oracle will adjust their terms to keep the money flowing in, but over the longer term I think willing Java developers are going to be hard to find.

Guido van Rossum recently removed himself from the post of Python’s Benevolent Dictator For Life. One of the jobs of a benevolent dictator is maintaining some degree of language coherence, which involves preventing people’s pet ideas from being added to the language. Does this mean that Python is slowly going to be become more and more bloated? Perhaps, but I think a more likely problem is a language fork, multiple implementations of slightly different (at first) languages all claiming to be Python.

These days, the strength of Python is its large collection of very useful, commercial grade, packages, and future language details may turn out to be irrelevant. There is a lot to learn from the Python 2/3 transition, but true believers like to think that things will turn out differently for them.

## Filters to help decide who might be a software developer

How do you find people who are likely to be good software developers?

I use the filter approach: start with whoever is available, filter out those who are not likely candidates and go with those that are left (if any).

The first filter is a question: which language do you like to program in?

This question is positive, in that it assumes the other person is a developer; asking for the name of a language makes it a difficult to dodge question for those who don’t know any language. The language itself is irrelevant, apart from as a lead in to further discussion.

Learning to program is easy and a fun thing to do, at least if you are the kind of person likely to become a good developer. Cheap computing hardware has been available since the 1980s, the extra ingredients are a desire to write software and some degree of the necessary skills.

The next filter is a discussion about the largest software system they have written.

The theme of the discussion is how they solved the problems encountered during the implementation. Do the problems sound like something a developer of the person’s experience ought to find a problem? How much perseverance was shown in solving the problems, were they flexible in trying alternatives, what was their approach to problem solving?

Building systems is all about solving problems. People who cannot solve problems will fail, those with problem solving abilities might succeed.

Demand for developers continues to outstrip supply, creating an opportunity for turkeys to fly.

When getting a university degree was intellectually challenging, it was a sign of cognitive firepower. The stated aim of the UK government is for 50% of 18-year olds to study for a degree, which means that courses requiring high cognitive firepower are dumbed down (otherwise the failure rate goes through the roof and a University’s ranking suffers). If the only option is a turkey shoot, a degree in a subject requiring lots mathematical thinking (e.g., physics, chemistry, some psychology subjects, …) is obviously a much better filter than Medieval French, Modern History, etc.

There are people whose path through life has kept them away from computers when they were younger and university when they were a bit older. Software carpentry seems to be doing good things for such people; I don’t have any direct experience of working with those who have gone that route, and so cannot say anything about it.

Will this filter approach work for you? Well, it depends on the characteristics required of a good developer in your line of work.

Perhaps you need a regular Joe, who does the job, nine-to-five, and sticks to the tried and trusted approached; a solid person who keeps systems reliably maintained and customers happy.

The independent, frontier, mentality that thrives in ‘new’ fields is becoming a less tolerated in software development. The frontier shrinks as more and more software becomes good-enough and those with money to pay for change, spend it on something else.

## Instructions that cpus don’t need to support

What instructions can computers do without (an earlier post covered instructions they should support)?

The R in RISC was supposed to stand for Reduced, but in practice almost all the instructions you would expect were supported. What was missing were the really complicated instructions that machines of the time (last 1980s), like the VAX, supported (analysis of instruction set usage showed that these complicated instructions were rarely used; from the compiler perspective the combination sequence of operations supported by these instructions rarely occurred in code).

One instruction that was often missing from the early RISC processors was integer multiply. Compilers were expected to generate a series of instructions that had the same effect. Some of the omitted ‘basic’ instructions got added to later versions of the processors that survived commercially (e.g., SPARC).

The status register is still a common omission from RISC designs (at least for the integer operations). Where is the data showing that in the grand scheme of things (i.e., processor performance running real programs), status registers slow things down? I know that hardware designers don’t like them because they introduce bottlenecks. I don’t recall ever having seen an analysis of instruction set usage targeted at the impact of status registers on generated code. Pointers welcome.

These days, nobody seems to analyze instruction set usage like they did in times past. Perhaps Intel’s marketing and the demise of almost every cpu vendor has dampened enthusiasm for researching new cpu designs. These days most new cpu designs seem to be fashion driven, rather than data driven.

Do computers need registers? An issue that once attracted lots of research was the optimal number of registers for a processor. The minimum number of registers (or temporary storage locations) needed to evaluate an expression was known by 1970. There were various studies of the impact, on code generation, of increasing/decreasing the number of registers available to the compiler. But these studies were done using 1990s era compilers and modern compilers do many more optimizations; whole program optimization ought to be able to make use of many more registers than are probably available on today’s processors (at least I think so, until somebody does a study that shows otherwise). There is a register-less processor that is supposed to be taking the world by storm, sometime soon.

Do computers need to support the IEEE floating-point representation? Logarithmic number systems are starting to be used in various devices, but accuracy remains an issue for some applications.

## Software engineering is fertile ground for the belief in silver bullets

The idea that there exists some wonderful technique or methodology, which solves one or more perceived software engineering problems, was given a name in 1986; the title of Brooks’ paper No Silver Bullet is a big clue that the author does not think it exists. Indeed, over the years a steady stream of papers have attempted to dispel the idea that silver bullets exist. These attempts have two things in common: the use of reasoning and facts to make their case, and failure to dispel the idea that there are no silver bullets in software engineering.

Now, I am a great fan of reasoning using facts, but I am also a fan of evidence driven approaches to solving problems. There is now over 30 years of evidence that reasoning using facts is not an effective means of convincing people that silver bullets don’t exist.

Belief in silver bullets will not go away until it ceases to be in some peoples’ interest for them to exist.

If you have something to sell, there is a benefit to having customers believe in silver bullets: the product/research will dramatically improve performance, time to market, costs, profitability, etc…

Belief in silver bullets is not unfounded. Computing has a 70-year history of things going faster, getting cheaper and systems doing what was once thought impossible. The press has bought into this and amazing success stories abound. Having worked on a few projects that delivered faster/cheaper/impossible systems, I know that no silver bullets were involved, just lots of hard work and sometimes being in the right place at the right time. Hard work and happenstance don’t make for feel-good headlines, and rarely get mentioned in the press.

When faced with a problem, the young and inexperienced tend to be optimists; there must be a silver bullet better way of doing this that is fast/cheap/efficient. The computing field has been evolving so rapidly that many of those involved are young and inexperienced; fertile ground for belief in silver bullets to flourish.

Consequences of a belief in silver bullets in industry include, time/cost overruns on projects and money wasted on tools that are never used. In academia a belief in silver bullets results in the pointless invention of new programming languages, methodologies, programming techniques, etc.

The belief in silver bullets will not fade away until the rate of change in computing slows to a crawl and most of those involved have gained substantial experience from which they can see that results come from hard work (and some amount of luck).

## Time taken to compile a source file

How long will it take to compile a source file?

When computers were a lot slower than they are today, this question was of general interest. Job scheduling is more effective when reliable runtime estimates are available, and developers want to know if there is enough time to get a coffee before the compile finishes.

An embarrassing fact about compile time performance, used to be that a large percentage of compile time was spent doing lexical analysis [“The cost of lexical analysis”, I cannot find an online copy]. Why was this embarrassing? Compiler writers like to boast about all the fancy optimizations their compiler does; but doing fancy stuff consumes lots of resources, so why were compilers spending so much of their time doing simple things like lexical analysis? The reality was that fancy compiler optimizations were not commercially viable until developer computers contained tens of megabytes of memory, i.e., very few pre-1990 compilers did any real optimization (people are still fussing over lexer performance).

An analysis of the data in Captain Dennis Miller’s Masters thesis (late Rome period), finds compile time is proportional to the square root of the number of tokens in the source (code+data); more complicated models are a slightly better fit. Where did square root come from? I expected a linear relationship, but would be willing to go with log. The measurements are from Ada compilers in the mid 1980s. I know several people who worked on Ada compilers during that time, and they were implementing the latest fancy optimizations (Ada was going to be the next big thing and the venture capital was flowing; big companies, with big computers were going to be paying lots of money to use Ada, but then microcomputers came along). I think that square root is driven by OS resource limitations, the compilers are using lots of memory and a noticeable amount of time is spent swapping.

So computers got a lot faster and people lost interest in estimates of how long it would take to compile individual files. I have not seen any interest in predicting how long it would take to compile whole projects (just complaints about how long it takes). There has been some work on progress indicators, updated as compilation progresses, which is a step in the right direction. Perhaps somebody has recorded compile time information and thrown machine learning at it; I usually ignore machine learning papers applied to software engineering and perhaps I have missed something. Pointers to project compile time prediction work welcome.

Then along came just-in-time compilation. Now people want to estimate how long it will take to generate machine code from some intermediate form, that is being interpreted.

The plot below (thanks to Rafael Auler for kindly supplying the data from his paper) shows the time taken to generate code from functions containing a given number of LLVM instructions (an intermediate code), at optimization level O3. The red line is a regression fit to one of the ‘arms’ and shows constant time for less than 100’ish instructions and then a linear relationship. I have no idea why the time is roughly constant for a large number of functions.

There is a lot of variation for function containing the same number of instructions. This is to be expected when lots of different optimizations are being tried; sometimes a function will contain lots of the kind of code that a particular optimization spends lot of times process and sometimes the code will not contain anything interesting (i.e., no optimizations are found).

## Undergraduates and learning to program

I last looked at the research on teaching programming around 10 years ago and I have been catching up with what has been going on; in brief: same old, same old. One of the best papers on the subject is still: Language-independent conceptual “Bugs”

The research activity is still focused on making the tools and language ‘better’. There is a defining silence on the possibility that those doing the teaching could not teach their way out of a paper bag. Nobody is brave enough to suggest that teacher training might be a worthwhile investment, or that lectures oriented to what is useful (rather than what the lecturer finds interesting) would be appreciated by students.

I have always thought that researching the teaching programming had no practical purpose, other than possibly helping universities increase the number of students graduating with computing degrees (some universities are solving the problem students have with programming by offering degrees that don’t involve being able to program). I still think that teaching programming to school children is at best a waste of time.

My experience with students learning to program is from a very long time ago. The process involved listening to confusing and disjoint lectures, reading books and figuring out what worked by trial and error. Students were not taught to program, they got thrown in at the deep and were expected to survive. Anybody who could handle this stood some chance of being able to handle developing software in the ‘real world'; universities were (accidentally) graduating people with the skills industry needed. However, these days universities are supposed to be customer focused, what industry needs to irrelevant (my experience of sitting on departmental industry panels is that the head of department tells us what they are thinking of doing {i.e., new courses for which there will be lots of paying students} and we try to talk him/her out of the sillier ideas); too many fee paying students find programming too hard, let’s offer computing degrees that don’t require any programming.

Would you hire a recent graduate, for a development role, who had trouble figuring out how to fix syntax errors in their code? Surely, the minimum requirement is somebody who gets some pleasure from coding, even if they don’t want to spend lots of time doing it.

There is a shortage of software developers and flying turkeys are still with us.

## Number of parameters vs. accessing globals

I spend a lot of time looking at software engineering data, asking, what is the story here?

In a previous post I suggested that the distribution of the number of functions defined to have a given number of parameters, might be a signature of developer beliefs about the relative cost of parameter passing vs accessing globals.

Looking at the data that Iran Rodrigues Gonzaga Junior made available (good man), as part of his thesis Empirical Studies on Fine-Grained Feature Dependencies, I saw it contained information about the number of parameters in a function definition and whether functions accessed a global (Gonzaga’s research question is in another direction; I am always repurposing data).

Are functions that access globals, defined with fewer parameters, compared to those that do not contain any such access? The plot below shows a count of the number of functions defined to have a given number of parameters, for four systems written in C; the solid lines are functions that did not access globals, the dashed lines are functions that accessed globals (code+data).

Over all 50 projects measured, functions that don’t access globals are defined, on average, to have an extra 0.7 parameters (the fitted Poisson regression models are better than a poke in the eye {i.e., the distribution is not really Poisson}, it’s more informative to look at the plotted data).

There is a lot of variation between projects (I picked these four because they were the larger projects and showed variation in behaviors). While the shape of the distributions varies a lot, there is always a noticeable difference in the mean.

Is this difference between projects a difference in developer beliefs, a difference in application requirements, a difference in developer coding habits (and parameter usage is a side effect; are there really that many getters and setters)?

I was hoping for a simple answer, and could not find one. Since I am writing a book and not researching individual issues in detail, it’s time to move on.

Ideas welcome.

## Main memory: the crucial component that vendors don’t mention

CPU performance hogs the limelight when people discuss the year-on-year increases in computing power that used to occur.

This focus on cpu performance was/is driven by marketing, the people with the money either don’t want customers thinking about the performance impact of main memory size or speed, or want them to treat the processor as the most important component of a computer. Vendors want processor performance to drive customer purchase decisions.

Hardware manufacturers used to entice new customers with low cost machines, containing minimal memory. Once a customer started to use their shiny new computer, they found that it did save them lots of time and money, but also they needed more memory (which could only be brought from the manufacturer and was not cheap).

The plot below shows the prices IBM charged for System 360s, in 1966. Anti-trust investigations uncover all kinds of interesting data, like selling low-spec equipment at a loss to entice customers and make life difficult for competitors (code+data for all plots).

The plot below (data from the 19 Aug 1985 issue of ComputerWorld) shows how the price of computers increased as the minimum about of memory they supported increased.

Yes, in 1985 top end computers came with over 50M of memory; but most customers thought themselves lucky if they had a few megabytes.

If the processor is slow, it just takes longer for programs to run. If the computer does not have enough memory, programs cannot run. For most applications memory requirements are addressed first, followed by processor performance; memory requirements is the number one issue. The optimizations that commercial compilers could perform were limited by the memory capacity of developer machines.

Intel’s main line of business used to be selling memory chips, but these chips became commodity items as more companies entered the market; Intel bet the farm on selling processors and the rest is history. As a seller of a unique product it was/is in Intel’s interest to spend lots of money on marketing the benefits of processor performance; sellers of commodity items (such as memory chips) don’t have nearly as much to gain from generic product marketing, because customers may choose to buy from other sellers (in such markets sellers have to concentrate on marketing themselves).

Memory capacity/speed and cpu speed are two aspects of system performance; they need to be balanced to meet customer drive application requirements. The plot below shows the SPEC cpu integer performance of 4,332 systems running at various clock rates; the colors denote the different peak memory transfer rates of the memory chips in these systems (code+data).

These days (and perhaps in the past, I don’t have any data), memory performance is a much better predictor of system performance, but vendors don’t have an incentive to market this fact.

## EDG and Github are both logical purchases for Microsoft

It looks like my prediction that Microsoft buys Github may be about to come true.

Microsoft has been sluggish in integrating their LinkedIn purchase into their identity management system. Lots of sites have verify identity using Github options (or at least the kind of sites I visit do), so perhaps LinkedIn identity will be trialed via Github.

A Github purchase will also allow Microsoft to directly connect lots of developers to Azure. Being able to easily build and execute Github code on Azure is the bait, customer data is where the money is; making Github more data friendly is an obvious first priority for new owners.

Who else should Microsoft buy? As a protective move, I think they should snap up Edison Design Group (EDG) before somebody else does. Readers outside of the compiler/static analysis/C++ standards world are unlikely to have heard of EDG. They sell C/C++ front ends (plus other languages) that support all the historical features/warts supported by other C/C++ compilers. The features only found in Microsoft’s compilers is what make it very costly/time-consuming for many companies to port their applications to other platforms; developer use of Microsoft compiler dependent features is a moat that makes it difficult for many companies to leave the Microsoft ecosystem. EDG have been in the business a long time and have built up an extensive knowledge of vendor specific compiler features; the kind of knowledge that can only be obtained by having customers tell you what language constructs they are using that your current product does not handle (and what those constructs actually mean).

What would happen if a very large company bought EDG, and open sourced its code (to make it easier for Windows developers to switch platforms, not to make any money off compiler related tools)? Somebody would have to bolt on a back-end, to generate code; but that would not be hard (EDG have designed their product to make this easy). A freely available compiler, supporting all/most of the foibles of the Microsoft C++ compiler, would tempt many Windows only developers to give it a go. A free compiler removes management from the loop, developers can try things out as a side project, without having to get management approval to spend money on a compiler (from practical experience I know how hard it is to sell compatible compiler products, i.e., there is no real money to be made by anybody doing this commercially).

Is this risk, to Microsoft, really worth the (relatively) low cost of buying EDG? The EDG guys are not getting any younger, why wouldn’t they be willing sell?

## The age of the Algorithm is long gone

I date the age of the Algorithm from roughly the 1960s to the late 1980s.

During the age of the Algorithms, developers spent a lot of time figuring out the best algorithm to use and writing code to implement algorithms.

Knuth’s The Art of Computer Programming (TAOCP) was the book that everybody consulted to find an algorithm to solve their current problem (wafer thin paper, containing tiny handwritten corrections and updates, was glued into the library copies of TAOCP held by my undergraduate university; updates to Knuth was news).

Two developments caused the decline of the age of the Algorithm (and the rise of the age of the Ecosystem and the age of the Platform; topics for future posts).

• The rise of Open Source (it was not called this for a while), meant it became less and less necessary to spend lots of time dealing with algorithms; an implementation of something that was good enough, was available. TAOCP is something that developers suggest other people read, while they search for a package that does something close enough to what they want.
• Software systems kept getting larger, driving down the percentage of time developers spent working on algorithms (the bulk of the code in commercially viable systems deals with error handling and the user interface). Algorithms are still essential (like the bolts holding a bridge together), but don’t take up a lot of developer time.

Algorithms are still being invented and some developers spend most of their working with algorithms, but peak Algorithm is long gone.

Perhaps academic researchers in software engineering would do more relevant work if they did not spend so much time studying algorithms. But, as several researchers have told me, algorithms is what people in their own and other departments think computing related research is all about. They remain shackled to the past.

## Premium mediocrity is software engineering’s demographic

Software engineering is one of the skills needed to write software, but outside of student coursework is rarely an end in itself. Software is written to do something and the person writing the code needs to know about the something.

If enough people are involved in something, a job title gets created by inserting the appropriate application domain name before ‘software engineer’, e.g., the something software engineer; systems software engineering was one of the first recorded uses of ‘software engineering’, ’embedded software engineer’ is a common usage and more recently ‘research software engineer’ has been trending.

Customers want the software systems they use to fulfill their needs. Implementing a software system involves figuring out what the needs are, how best to implement them using the available resources and producing usable software; all within a given amount of time and money.

How much software engineering knowledge and skill does a something software engineer need? The obvious answer is: enough to get the something done. Ok, how much is needed to get the something done?

There are so many hours in a day: what percentage of available time is best spent learning about software engineering, what percentage leaning about the something and what percentage doing rather than learning?

The only data I have for answering this question is my own experience of talking to people, from a wide range of business and application areas, whose job includes writing software. My background is compilers (from C to Cobol) and static analysis, my knowledge of end-user application domains is derived from talking to the developers who were using the compilers or static analysis tools I was working on at the time.

I have always been struck by the minimalist knowledge of most developers, when it comes to the programming language they were using. It took a while, but eventually I accepted the obvious: most developers don’t need to know much about the language they are using to get their job done.

By a process that resembles incentivized trial and error, people learn how to write code that does what they want; the compiler does not complain and the output looks ok. For some languages, I used to be able to work out which books a relatively new developer had used to guide their learning, by matching a book’s example code snippets with the code they had written.

This minimalist knowledge approach to programming languages is cost effective because most code is simple and has a short lifetime; the cost of learning lots of language details does not provide enough benefit to be worthwhile.

I am a minimalist language Python developer. Why would I spend time learning more about the semantics of Python than I need to?

What are the benefits of being a language expert? Compiler writers get paid to learn the ins and outs of a language and I know a few people who became language experts without being compiler writers (they got hooked on knowing the language). I have found it useful for keeping my code simple (I am not tempted to write complicate code, or use obscure constructs, in the mistaken belief that they are better than the simple stuff), it is also useful for figuring out other people’s complicated or obscure usage (created intentionally or accidentally).

I have occasionally taught advanced programming courses, aimed at developers with a few years experience working in industry. These courses had to include the word ‘advanced’ in their title, otherwise developers with a few years experience would never have signed-up; ‘advanced’ is a necessary marketing signal (others who have run such courses report the same behavior). The course contents were essentially a review of basic material, with lots of examples; most of those attending did not know enough to follow real advanced material. The courses were really about uncovering and correcting bad habits that attendees had picked up over time (often, a technique was discovered to fix a problem and then subsequently adopted for more general use).

What about general software engineering skills? A minimalist knowledge approach to software engineering is cost effective because most code does not exist long enough to make it worthwhile investing in reducing future maintenance costs. Yes, it is more expensive for those that survive to become commonly used, but think of all the savings from not investing in those that did not survive. Software engineering decisions should not be driven by surviorship bias.

The first requirement of any commercial software system is to attract paying customers. In a rapidly changing market, being first with a saleable product can be the difference between life and death. Minimizing software engineering effort saves time and money (in the short term). If the product is a success, there will be money to pay for what needs to be done, if the product fails nobody cares. I have seen a lot of software systems that are a commercial success and a complete software engineering mess; successful, well engineered software is less common (or perhaps they just don’t need me to help them out).

Software engineering mediocrity is not only viable, for most people it’s the outcome of making a cost/benefit decision to invest their learning time in the application domain, not software engineering (or computer language).

Of course, nobody wants to be seen as being mediocre (for some people, mediocre overstates their skill level); their behavior is premium mediocre.

There are a few application areas where software engineering skills are needed, e.g., safety critical software and warehouse scale computing. A few high profile cases are hiding the reality that whatever works is cost effective for most software solutions.

## Replicating results using research software

The reproducibility of results, from scientific studies, has always been an important issue. Over the last few years software has become a hot topic in reproducibility circles; many researchers have an expectation that if they run the original researcher’s software, they will replicate the results. Reality has not lived up to their expectations and there is lots of flapping around looking for a solution. There is a solution, but first, why does the problem exist?

I have spent a lot of time porting software to different compilers (when I was in the compiler business, I wanted everybody to port their applications to the compiler I was working), different hardware (oh, the days when every major vendor had at least one distinct cpu; not like today where it’s x86, ARM, or embedded), different operating systems (umpteen flavors of Unix, all with slightly different header file contents and library behavior; the Unix wars were good for those in the porting business) and every now and again different languages (by translating).

The Wintel alliance wiped out variation in cpus and operating systems (they can still be found lurking in dark corners) and open source compilers created a near monoculture of compilers for the major languages.

The major software portability problems of 30 years ago have become rather minor. But software portability problems that once tended to be minor (at least for scientific software), have grown to become a major headache. Today’s major portability problems center around evolution of the libraries/packages being used, and longer term the evolution of the language(s) used.

Evolution has created development ecosystems where there are rampant dependencies on specific, or earlier than, or later than versions of libraries/packages. I have been out of the porting business for several decades, but talking to those doing it today, the story is the same; experience in porting from A to B is everything, second best is talking to somebody else who has gone in that direction and third best are the one-line forums such as stackoverflow.

Researchers are doing research on who-knows-what and probably have need-to-know knowledge of software and the libraries they are using, the researchers receiving a copy of the original software might know less. What is the probability that the originating and receiving researchers have exactly versions of libraries installed? The receiving researcher may not have any of the needed libraries installed, and promptly install the latest version (which may well be more recent than the one used by the original researcher).

A solution is available; distribute a duplicate of the researchers complete system as a container, e.g., a Docker image.

Containers solve the replication problem. But these days people want more, they actually think it should be possible to take research software and modify it to suite their own needs. Good luck with that.

Research software is written to solve a problem, often by people writing their first non-trivial programs (i.e., they are novices), with no incentive to produce something that is easy for others to use. When software is written by experienced developers, who have an incentive to build something that is easy for others to work with, multiple reimplementations are often still required to achieve something of decent quality. Creating robust software, that others can use, is very hard.

The problem with software is its invisibility; the difficulties are not visible. When the internal operations are visible, the difficulties of making changes are easier to see.

James Albert Bonsack’s cigarette rolling machine (from Wikipedia).

## Type compatibility: name vs structural equivalence

What are the rules for deciding when two types are the same, or compatibility?

This question needs to be answered to decide whether an object of type `T1` can be assigned to an object of type `T2`, whether they can be compared, added together, etc.

A wide collection of rules have been combined together, by various languages, for type compatibility of scalar types (e.g., integer, character, etc), but for aggregate types two rules dominate: name equivalence, and structural equivalence, or some combination.

With name equivalence, two types are the same if they are declared using the same name (e.g., the name of the tag for a union type, in C)

With structural equivalence, two types are compatible if they have a compatible structure, i.e., their internal contents are type compatible (this requires walking over each field/member checking that it is compatible). For instance, an object declared to have an aggregate type containing three integers is compatible with another aggregate type containing three integers (assuming any type modifiers, such as const’ness or mutability, are the same).

Structural compatibility becomes interesting when pointer types are involved; the pointed to types need to be checked and loops can occur, e.g., type `S1` contains a field that has a type pointer to `S2`, which contains a field that has type pointer to `S1`.

While most types are easily checked for structural compatibility, every now and again aggregate types connect together in a way that makes it non-trivial to figure out which types are structurally compatible (dot file; needs graphviz):

Handling the edge cases requires maintaining a stack of information about which pairs of types are currently being compared.

In C, type compatibility is a combination of name equivalence (for aggregate types in the same translation unit) and structural equivalence (for function types and aggregate types across translation units).

Function types have to use structural equivalence because the type in a function definition is anonymous (the function name that appears in the definition has this anonymous type), there is no name to compare.

Cycles cannot appear in function types (in C), because the identifier being defined in a `typedef` is not in scope until just after the completion of its declarator. It is not possible to refer to the identifier being defined insider its own definition (e.g., it is not possible to define a function that takes its own type as a parameter; in `typedef int (*f)(f);` the second `f` is a redundant parameter name, the scope of the type denoted by the first `f` begins just before the semicolon).

Structural equivalence across translation units is a hangover from the early days of C, when developers were sloppy when using (or not) tag names (with different people having different rules for using upper/lower case tag names); developers’ knew what the layout in memory was and created the necessary types for their use of this data.

Type compatibility via name equivalence is easy to explain and makes it explicit when developers are bending the rules (i.e., pointer to `struct` casts appear in the code).

Type compatibility via structural equivalence is the wild west, which still exists in some development environments.

## Replication: not always worth the effort

Replication is the means by which mistakes get corrected in science. A researcher does an experiment and gets a particular result, but unknown to them one or more unmeasured factors (or just chance) had a significant impact. Another researcher does the same experiment and fails to get the same results, and eventually many experiments later people have figured out what is going on and what the actual answer is.

In practice replication has become a low status activity, journals want to publish papers containing new results, not papers backing up or refuting the results of previously published papers. The dearth of replication has led to questions being raised about large swathes of published results. Most journals only published papers that contain positive results, i.e., something was shown to some level of statistical significance; only publishing positive results produces publication bias (there have been calls for journals that publishes negative results).

Sometimes, repeating an experiment does not seem worth the effort. One such example is: An Explicit Strategy to Scaffold Novice Program Tracing. It looks like the authors ran a proper experiment and did everything they are supposed to do; but, I think the reason that got a positive result was luck.

The experiment involved 24 subjects and these were randomly assigned to one of two groups. Looking at the results (figures 4 and 5), it appears that two of the subjects had much lower ability that the other subjects (the authors did discuss the performance of these two subjects). Both of these subjects were assigned to the control group (there is a 25% chance of this happening, but nobody knew what the situation was until the experiment was run), pulling down the average of the control, making the other (strategy) group appear to show an improvement (i.e., the teaching strategy improved student performance).

Had one, or both, low performers been assigned to the other (strategy) group, no experimental effect would have shown up in the results, significantly reducing the probability that the paper would have been accepted for publication.

Why did the authors submit the paper for publication? Well, academic performance is based on papers published (quality of journal they appear in, number of citations, etc), a positive result is reason enough to submit for publication. The researchers did what they have been incentivized to do.

I hope the authors of the paper continue with their experiments. Life is full of chance effects and the only way to get a solid result is to keep on trying.

## A Python Data Science hackathon

I was at the Man AHL Hackathon this weekend. The theme was improving the Python Data Science ecosystem. Around 15, or so, project titles had been distributed around the tables in the Man AHL cafeteria and the lead person for each project gave a brief presentation. Stable laws in SciPy sounded interesting to me and their room location included comfy seating (avoiding a numb bum is an under appreciated aspect of choosing a hackathon team and wooden bench seating is not numbing after a while).

Team Stable laws consisted of Andrea, Rishabh, Toby and yours truly. Our aim was to implement the Stable distribution as a Python module, to be included in the next release of SciPy (the availability had been announced a while back and there has been one attempt at an implementation {which seems to contain a few mistakes}).

We were well-fed and watered by Man AHL, including fancy cream buns and late night sushi.

A probability distribution is stable if the result of linear combinations of the distribution has the same distribution; the Gaussian, or Normal, distribution is the most well-known stable distribution and the central limit theorem leads many to think, that is that.

Two other, named, stable distributions are the Cauchy distribution and most interestingly (from my perspective this weekend) the Lévy distribution. Both distributions have very fat tails; the mean and variance of the Cauchy distribution is undefined (i.e., the values jump around as the sample size increases, never converging to a fixed value), while they are both infinite for the Lévy distribution.

Analytic expressions exist for various characteristics of the Stable distribution (e.g., probability distribution function), with the Gaussian, Cauchy and Lévy distributions as special cases. While solutions for implementing these expressions have been proposed, care is required; the expressions are ill-behaved in different ways over some intervals of their parameter values.

Andrea has spent several years studying the Stable distribution analytically and was keen to create an implementation. My approach for complicated stuff is to find an existing implementation and adopt it. While everybody else worked their way through the copious papers that Andrea had brought along, I searched for existing implementations.

I found several implementations, but they all suffered from using approaches that delivered discontinuities and poor accuracies over some range of parameter values.

Eventually I got lucky and found a paper by Royuela-del-Val, Simmross-Wattenberg and Alberola-López, which described their implementation in C: Libstable (licensed under the GPL, perfect for SciPy); they also provided lots of replication material from their evaluation. An R package was available, but no Python support.

No other implementations were found. Team Stable laws decided to create a new implementation in Python and to create a Python module to interface to the C code in `libstable` (the bit I got to do). Two implementations would allow performance and accuracy to be compared (accuracy checks really need three implementations to get some idea of which might be incorrect, when output differs).

One small fix was needed to build `libstable` under OS X (change `Makefile` to link against `.so` library, rather than `.a`) and a different fix was needed to install the R package under OS X (R patch; Windows and generic Unix were fine).

Python’s ctypes module looked after loading the C shared library I built, along with converting the NumPy arrays. My PyStable module will not win any Python beauty contest, it is a means of supporting the comparison of multiple implementations.

Some progress was made towards creating a new implementation, more than 24 hours is obviously needed (`libstable` contains over 4,000 lines of code). I had my own problems with an exception being raised in calls to `stable_pdf`; `libstable` used the GNU Scientific Library and I tracked the problem down to a call into GSL, but did not get any further.

We all worked overnight, my first 24-hour hack in a very long time (I got about 4-hours sleep).

After Sunday lunch around 10 teams presented and after a quick deliberation, Team Stable laws were announced as the winners; yea!

Hopefully, over the coming weeks a usable implementation will come into being.

## Influential philosophers of source code

Who is the most important/influential philosopher of source code? Source code, as far as I know, is not a subject that philosophers claim to be studying; but, the study of logic, language and the mind is the study of source code.

For many, Ludwig Wittgenstein would probably be the philosopher that springs to mind. Wittgenstein became famous as the world’s first Perl programmer, with statements such as: “If a lion could talk, we could not understand him.” and “Whereof one cannot speak, thereof one must be silent.”

Noam Chomsky, a linguist, might be another choice, based on his specification of the Chomsky hierarchy (which neatly categorizes grammars). But generative grammars (for which he is famous in linguistics) is about generating language, not understanding what has been said/written.

My choice for the most important/influential philosopher of source code is Paul Grice. A name, I suspect, that is new to most readers. The book to quote (and to read if you enjoy the kind of books philosophers write) is “Studies in the Way of Words”.

Grice’s maxims, provide a powerful model for human communication; the tldr:

• Maxim of quality: Try to make your contribution one that is true.
• Maxim of quantity: Make your contribution as informative as is required.
• Maxim of relation: Be relevant.

But source code is about human/computer communication, you say. Yes, but so many developers seem to behave as-if they were involved in human/human communication.

Source code rarely expresses what the developer means; source code is evidence of what the developer means.

The source code chapter of my empirical software engineering book is Gricean, with a Relevance theory accent.

More easily digestible books on Grice’s work (for me at least) are: “Relevance: Communication and Cognition” by Sperber and Wilson, and the more recent “Meaning and Relevance” by Wilson and Sperber.

## The C++ committee has taken off its ball and chain

A step change in the approach to updates and additions to the C++ Standard occurred at the recent WG21 meeting, or rather a change that has been kind of going on for a few meetings has been documented and discussed. Two bullet points at the start of “C++ Stability, Velocity, and Deployment Plans [R2]”, grab reader’s attention:

● Is C++ a language of exciting new features?
● Is C++ a language known for great stability over a long period?

followed by the proposal (which was agreed at the meeting): “The Committee should be willing to consider the design / quality of proposals even if they may cause a change in behavior or failure to compile for existing code.”

We have had 30 years of C++/C compatibility (ok, there have been some nibbling around the edges over the last 15 years). A remarkable achievement, thanks to Bjarne Stroustrup over 30+ years and 64 full-week standards’ meetings (also, Tom Plum and Bill Plauger were engaged in shuttle diplomacy between WG14 and WG21).

The C/C++ superset/different issue has a long history.

In the late 1980s SC22 (the top-level ISO committee for programming languages) asked WG14 (the C committee) whether a standard should be created for C++, and if so did WG14 want to create it. WG14 considered the matter at its April 1989 meeting, and replied that in its view a standard for C++ was worth considering, but that the C committee were not the people to do it.

In 1990, SC22 started a study group to look into whether a working group for C++ should be created and in the U.S. X3 (the ANSI committee responsible for Information processing systems) set up X3J16. The showdown meeting of what would become WG21, was held in London, March 1992 (the only ISO C++ meeting I have attended).

The X3J16 people were in London for the ISO meeting, which was heated at times. The two public positions were: 1) work should start on a standard for C++, 2) C++ was not yet mature enough for work to start on a standard.

The, not so public, reason given for wanting to start work on a standard was to stop, or at least slow down, changes to the language. New releases, rumored and/or actual, of Cfront were frequent (in a pre-Internet time sense). Writing large applications in a version of C++ that was replaced with something sightly different six months later was has developers in large companies pulling their hair out.

You might have thought that compiler vendors would be happy for the language to be changing on a regular basis; changes provide an incentive for users to pay for compiler upgrades. In practice the changes were so significant that major rework was needed by somebody who knew what they were doing, i.e., expensive people had to be paid; vendors were more used to putting effort into marketing minor updates. It was claimed that implementing a C++ compiler required seven times the effort of implementing a C compiler. I have no idea how true this claim might have been (it might have been one vendor’s approximate experience). In the 1980s everybody and his dog had their own C compiler and most of those who had tried, had run into a brick wall trying to implement a C++ compiler.

The stop/slow down changing C++ vs. let C++ “fulfill its destiny” (a rallying call from the AT&T rep, which the whole room cheered) finally got voted on; the study group became a WG (I cannot tell you the numbers; the meeting minutes are not online and I cannot find a paper copy {we had those until the mid/late-90s}).

The creation of WG21 did not have the intended effect (slowing down changes to the language); Stroustrup joined the committee and C++ evolution continued apace. However, from the developers’ perspective language change did slow down; Cfront changes stopped because its code was collapsing under its own evolutionary weight and usable C++ compilers became available from other vendors (in the early days, Zortech C++ was a major boost to the spread of usage).

The last WG21 meeting had 140 people on the attendance list; they were not all bored consultants looking for a creative outlet (i.e., exciting new features), but I’m sure many would be happy to drop the ball-and-chain (otherwise known as C compatibility).

I think there will be lots of proposals that will break C compatibility in one way or another and some will it into a published standard. The claim will be that the changes will make life easier for future C++ developers (a claim made by proponents of every language, for which there is zero empirical evidence). The only way of finding out whether a change has long term benefit is to wait a long time and see what happens.

The interesting question is how C++ compiler vendors will react to breaking changes in the language standard. There are not many production compilers out there these days, i.e., not a lot of competition. What incentive does a compiler vendor have to release a version of their compiler that will likely break existing code? Compiler validation, against a standard, is now history.

If WG21 make too many breaking changes, they could find C++ vendors ignoring them and developers asking whether the ISO C++ standards’ committee is past its sell by date.

## GDPR has a huge impact on empirical software engineering research

The EU’s General Data Protection Regulation (GDPR) is going to have a huge impact on empirical software engineering research. After 25 May 2018, analyzing source code will never be the same again.

I am not a lawyer and nothing qualifies me to talk about the GDPR.

People put their name in source code, bug tracking databases and discussion forums; this is personal identifying information.

Researchers use personal names to obtain information about a wide variety of activities, e.g., how much code did individuals write, how many bug reports did they process, contributions in discussions of one sort or another.

Open source licenses give others all kinds of rights (e.g., ability to use and modify source code), but they do not contain any provisions for processing personal data.

Adding a “I hereby give permission for anybody to process information about my name in any way they see fit.” clause to licenses is not going to help.

The GDPR requires (article 5: Principles relating to processing of personal data):

“Personal data shall be: … collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes;”

That is, personal data can only be processed for the specific reason it was collected, i.e., if you come up with another bright idea for analysis of data that has just been collected, it may be necessary to obtain consent, from those whose personal data it is, before trying out the bright idea.

It is not possible to obtain blanket permission (article 6, Lawfulness of processing):

“…the data subject has given consent to the processing of his or her personal data for one or more specific purposes;”, i.e., consent has to be obtained from the data subject for each specific purpose.

Github’s Global Privacy Practices shows that Github are intent on meeting the GDPR requirements, they include: “GitHub provides clear methods of unambiguous, informed consent at the time of data collection, when we do collect your personal data.”. Processing personal information, about an EU citizen, contained in source code appears to be a violation of Github’s terms of service.

The GDPR has many other requirements, e.g., right to obtain information on what information is held and right to be forgotten. But, the upfront killer is not being able to cheaply collect lots of code and then use personal information to help with the analysis.

There are exceptions for: Processing for archiving, scientific or historical research or statistical purposes. Can somebody who blogs and is writing a book claim to be doing scientific research? People who know more about these exceptions than me, tell me that there could be a fair amount of paperwork involved when making use of the exception, i.e., being able to show that privacy safeguards are in place.

Then, there is the issue of what constitutes personal information. Git’s hashing algorithm makes use of the committer’s name and/or email address. Is a git hash personal identifying information?

## Reliability chapter added to “Empirical software engineering using R”

The Reliability chapter of my Empirical software engineering book has been added to the draft pdf (download here).

I have been working on this draft for four months and it still needs lots of work; time to move on and let it stew for a while. Part of the problem is lack of public data; cost and schedule overruns can be rather public (projects chapter), but reliability problems are easier to keep quiet.

Originally there was a chapter covering reliability and another one covering faults. As time passed, these merged into one. The material kept evaporating in front of my eyes (around a third of the initial draft, collected over the years, was deleted); I have already written about why most fault prediction research is a waste of time. If it had not been for Rome I would not have had much to write about.

Perhaps what will jump out at people most, is that I distinguish between mistakes in code and what I call a fault experience. A `fault_experience=mistake_in_code + particular_input`. Most fault researchers have been completely ignoring half of what goes into every fault experience, the input profile (if the user does not notice a fault, I do not consider it experienced) . It’s incredibly difficult to figure out anything about the input profile, so it has been quietly ignored (one of the reasons why research papers on reported faults are such a waste of time).

I’m also missing an ‘interesting’ figure on the opening page of the chapter. Suggestions welcome.

I have not said much about source code characteristics. There is a chapter covering source code, perhaps some of this material will migrate to reliability.

All sorts of interesting bits and pieces have been added to earlier chapters. Ecosystems keeps growing and in years to come somebody will write a multi-volume tomb on software ecosystems.

I have been promised all sorts of data. Hopefully some of it will arrive.

As always, if you know of any interesting software engineering data, please tell me.

Source code chapter next.

## McCabe’s cyclomatic complexity and accounting fraud

The paper in which McCabe proposed what has become known as McCabe’s cyclomatic complexity did not contain any references to source code measurements, it was a pure ego and bluster paper.

Fast forward 10 years and cyclomatic complexity, complexity metric, McCabe’s complexity…permutations of the three words+metrics… has become one of the two major magical omens of code quality/safety/reliability (Halstead’s is the other).

It’s not hard to show that McCabe’s complexity is a rather weak measure of program complexity (it’s about as useful as counting lines of code).

Just as it is possible to reduce the number of lines of code in a function (by putting all the code on one line), it’s possible to restructure existing code to reduce the value of McCabe’s complexity (which is measured for individual functions).

The value of McCabe’s complexity for the following function is 16, i.e., there are 16 possible paths through the function:

```int main(void)
{
if (W) a(); else b();
if (X) c(); else d();
if (Y) e(); else f();
if (Z) g(); else h();
}
```

each `if``else` contains two paths and there are four in series, giving paths.

Restructuring the code, as below, removes the multiplication of paths caused by the sequence of `if``else`:

```void a_b(void) {if (W) a(); else b();}

void c_d(void) {if (X) c(); else d();}

void e_f(void) {if (Y) e(); else f();}

void g_h(void) {if (Z) g(); else h();}

int main(void)
{
a_b();
c_d();
e_f();
g_h();
}
```

reducing `main`‘s McCabe complexity to 1 and the four new functions each have a McCabe complexity of two.

Where has the ‘missing’ complexity gone? It now ‘exists’ in the relationship between the functions, a relationship that is not included in the McCabe complexity calculation.

The number of paths that can be traversed, by a call to `main`, has not changed (but the McCabe method for counting them now produces a different answer)

Various recommended practice documents suggest McCabe’s complexity as one of the metrics to consider (but don’t suggest any upper limit), while others go as far as to claim that it’s bad practice for functions to have a McCabe’s complexity above some value (e.g., 10) or that “Cyclomatic complexity may be considered a broad measure of soundness and confidence for a program“.

Consultants in the code quality/safety/security business need something to complain about, that is not too hard or expensive for the client to fix.

If a consultant suggested that you reduced the number of lines in a function by joining existing lines, to bring the count under some recommended limit, would you take them seriously?

What about, if a consultant highlighted a function that had an allegedly high McCabe’s complexity? Should what they say be taken seriously, or are they essentially encouraging developers to commit the software equivalent of accounting fraud?

## Top, must-read paper on software fault analysis

What is the top, must read, paper on software fault analysis?

Software Reliability: Repetitive Run Experimentation and Modeling by Phyllis Nagel and James Skrivan is my choice (it’s actually a report, rather than a paper). Not only is this report full of interesting ideas and data, but it has multiple replications. Replication of experiments in software engineering is very rare; this work was replicated by the original authors, plus Scholz, and then replicated by Janet Dunham and John Pierce, and then again by Dunham and Lauterbach!

I suspect that most readers have never heard of this work, or of Phyllis Nagel or James Skrivan (I hadn’t until I read the report). Being published is rarely enough for work to become well-known, the authors need to proactively advertise the work. Nagel, Dunham & co worked in industry and so did not have any students to promote their work and did not spend time on the academic seminar circuit. Given enough effort it’s possible for even minor work to become widely known.

The study run by Nagel and Skrivan first had three experienced developers independently implement the same specification. Each of these three implementations was then tested, multiple times. The iteration sequence was: 1) run program until fault experienced, 2) fix fault, 3) if less than five faults experienced, goto step (1). The measurements recorded were fault identity and the number of inputs processed before the fault was experienced.

This process was repeated 50 times, always starting with the original (uncorrected) implementation; the replications varied this, along with the number of inputs used.

For a fault to be experienced, there has to be a mistake in the code and the ‘right’ input values have to be processed.

How many input values need to be processed, on average, before a particular fault is experienced? Does the average number of inputs values needed for a fault experience vary between faults, and if so by how much?

The plot below (code+data) shows the numbers of inputs processed, by one of the implementations, before individual faults were experienced, over 50 runs (sorted by number of inputs):

Different faults have different probabilities of being experienced, with fault a being experienced on almost any input and fault e occurring much less frequently (a pattern seen in the replications). There is an order of magnitude variation in the number of inputs processed before particular faults are experienced (this pattern is seen in the replications).

Faults were fixed as soon as they were experienced, so the technique for estimating the total number of distinct faults, discussed in a previous post, cannot be used.

A plot of number of faults found against number of inputs processed is another possibility. More on that another time.

Suggestions for top, must read, paper on software faults, welcome (be warned, I think that most published fault research is a waste of time).

## Estimating the number of distinct faults in a program

In an earlier post I gave two reasons why most fault prediction research is a waste of time: 1) it ignores the usage (e.g., more heavily used software is likely to have more reported faults than rarely used software), and 2) the data in public bug repositories contains lots of noise (i.e., lots of cleaning needs to be done before any reliable analysis can done).

Around a year ago I found out about a third reason why most estimates of number of faults remaining are nonsense; not enough signal in the data. Date/time of first discovery of a distinct fault does not contain enough information to distinguish between possible exponential order models (technical details; practically all models are derived from the exponential family of probability distributions); controlling for usage and cleaning the data is not enough. Having spent a lot of time, over the years, collecting exactly this kind of information, I was very annoyed.

The information required, to have any chance of making a reliable prediction about the likely total number of distinct faults, is a count of all fault experiences, i.e., multiple instances of the same fault need to be recorded.

The correct techniques to use are based on work that dates back to Turing’s work breaking the Enigma codes; people have probably heard of Good-Turing smoothing, but the slightly later work of Good and Toulmin is applicable here. The person whose name appears on nearly all the major (and many minor) papers on population estimation theory (in ecology) is Anne Chao.

The Chao1 model (as it is generally known) is based on a count of the number of distinct faults that occur once and twice (the Chao2 model applies when presence/absence information is available from independent sites, e.g., individuals reporting problems during a code review). The estimated lower bound on the number of distinct items in a closed population is:

and its standard deviation is:

where: is the estimated number of distinct faults, the observed number of distinct faults, the total number of faults, the number of distinct faults that occurred once, the number of distinct faults that occurred twice, .

A later improved model, known as iChoa1, includes counts of distinct faults occurring three and four times.

Where can clean fault experience data, where the number of inputs have been controlled, be obtained? Fuzzing has become very popular during the last few years and many of the people doing this work have kept detailed data that is sometimes available for download (other times an email is required).

Kaminsky, Cecchetti and Eddington ran a very interesting fuzzing study, where they fuzzed three versions of Microsoft Office (plus various Open Source tools) and made their data available.

The faults of interest in this study were those that caused the program to crash. The plot below (code+data) shows the expected growth in the number of previously unseen faults in Microsoft Office 2003, 2007 and 2010, along with 95% confidence intervals; the x-axis is the number of faults experienced, the y-axis the number of distinct faults.

The take-away point: if you are analyzing reported faults, the information needed to build models is contained in the number of times each distinct fault occurred.

## Historians of computing

Who are the historians of the computing? The requirement I used for deciding who qualifies (for this post), is that the person has written multiple papers on the subject over a period that is much longer than their PhD thesis (several people have written history of some aspect of computing PhDs and then gone on to research other areas).

Maarten Bullynck. An academic who is a historian of mathematics and has become interested in software; use HAL to find his papers, e.g., What is an Operating System? A historical investigation (1954–1964).

Martin Campbell-Kelly. An academic who has spent his research career investigating computing history, primarily with a software orientation. Has written extensively on a wide variety of software topics. His book “From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry” is still on my pile of books waiting to be read (but other historian cite it extensively). His thesis: “Foundations of computer programming in Britain, 1945-55″, can be freely downloadable from the British Library; registration required.

James W. Cortada. Ex-IBM (1974-2012) and now working at the Charles Babbage Institute. Written extensively on the history of computing. More of a hardware than software orientation. Written lots of detail oriented books and must have pole position for most extensive collection of material to cite (his end notes are very extensive). His “Buy The Digital Flood: The Diffusion of Information Technology Across the U.S., Europe, and Asia” is likely to be the definitive work on the subject for some time to come. For me this book is spoiled by the author towing the company line in his analysis of the IBM antitrust trial; my analysis of the work Cortada cites reaches the opposite conclusion.

Nathan Ensmenger. An academic; more of a people person than hardware/software. His paper Letting the Computer Boys Take Over contains many interesting insights. His book The Computer Boys Take Over Computers, Programmers, and the Politics of Technical Expertise is a combination of topics that have been figured and back with references and topics still being figured out (I wish he would not cite Datamation, a trade mag back in the day, so often).

Michael S. Mahoney. An academic who is sadly no longer with us. A historian of mathematics before becoming involved with primarily software.

Jeffrey R. Yost. An academic. I have only read his book “Making IT Work: A history of the computer services industry”, which was really a collection of vignettes about people, companies and events; needs some analysis. Must try to track down some of his papers (which are not available via his web page :-(.

Who have I missed? This list is derived from papers/books I have encountered while working on a book, not an active search for historians. Suggestions welcome.

## Building a regression model is easy and informative

Running an experiment is very time-consuming. I am always surprised that people put so much effort into gathering the data and then spend so little effort analyzing it.

The Computer Language Benchmarks Game looks like a fun benchmark; it compares the performance of 27 languages using various toy benchmarks (they could not be said to be representative of real programs). And, yes, lots of boxplots and tables of numbers; great eye-candy, but what do they all mean?

The authors, like good experimentalists, make all their data available. So, what analysis should they have done?

A regression model is the obvious choice and the following three lines of R (four lines if you could the blank line) build one, providing lots of interesting performance information:

```cl=read.csv("Computer-Language_u64q.csv.bz2", as.is=TRUE)

cl_mod=glm(log(cpu.s.) ~ name+lang, data=cl)
summary(cl_mod)
```

The following is a cut down version of the output from the call to `summary`, which summarizes the model built by the call to `glm`.

```                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         1.299246   0.176825   7.348 2.28e-13 ***
namechameneosredux  0.499162   0.149960   3.329 0.000878 ***
namefannkuchredux   1.407449   0.111391  12.635  < 2e-16 ***
namefasta           0.002456   0.106468   0.023 0.981595
namemeteor         -2.083929   0.150525 -13.844  < 2e-16 ***

langclojure         1.209892   0.208456   5.804 6.79e-09 ***
langcsharpcore      0.524843   0.185627   2.827 0.004708 **
langdart            1.039288   0.248837   4.177 3.00e-05 ***
langgcc            -0.297268   0.187818  -1.583 0.113531
langocaml          -0.892398   0.232203  -3.843 0.000123 ***

Null deviance: 29610  on 6283  degrees of freedom
Residual deviance: 22120  on 6238  degrees of freedom
```

What do all these numbers mean?

We start with `glm`'s first argument, which is a specification of the regression model we are trying to fit: `log(cpu.s.) ~ name+lang`

`cpu.s.` is cpu time, `name` is the name of the program and `lang` is the language. I found these by looking at the column names in the data file. There are other columns in the data, but I am running in quick & simple mode. As a first stab, I though cpu time would depend on the program and language. Why take the `log` of the cpu time? Well, the model fitted using cpu time was very poor; the values range over several orders of magnitude and logarithms are a way of compressing this range (and the fitted model was much better).

The model fitted is:

, or

Plugging in some numbers, to predict the cpu time used by say the program `chameneosredux` written in the language `clojure`, we get: (values taken from the first column of numbers above).

This model assumes there is no interaction between program and language. In practice some languages might perform better/worse on some programs. Changing the first argument of `glm` to: `log(cpu.s.) ~ name*lang`, adds an interaction term, which does produce a better fitting model (but it's too complicated for a short blog post; another option is to build a mixed-model by using `lmer` from the `lme4` package).

We can compare the relative cpu time used by different languages. The multiplication factor for `clojure` is , while for `ocaml` it is . So `clojure` consumes 8.2 times as much cpu time as `ocaml`.

How accurate are these values, from the fitted regression model?

The second column of numbers in the `summary` output lists the estimated standard deviation of the values in the first column. So the `clojure` value is actually , i.e., between 2.2 and 4.9 (the multiplication by 1.96 is used to give a 95% confidence interval); the `ocaml` values are , between 0.3 and 0.6.

The fourth column of numbers is the p-value for the fitted parameter. A value of lower than 0.05 is a common criteria, so there are question marks over the fit for the program `fasta` and language `gcc`. In fact many of the compiled languages have high p-values, perhaps they ran so fast that a large percentage of start-up/close-down time got included in their numbers. Something for the people running the benchmark to investigate.

Isn't it easy to get interesting numbers by building a regression model? It took me 10 minutes, ok I spend a lot of time fitting models. After spending many hours/days gathering data, spending a little more time learning to build simple regression models is well worth the effort.

## Statement sequence length for error/non-error paths

One of the folk truisms of the compiler/source code analysis business is that error paths are short, i.e., when an error situation is detected (such as failing to open a file), few statements are executed before the functions returns.

Having repeated this truism for many decades, figure 2 from the paper APEx: Automated Inference of Error Specifications for C APIs jumped off the page at me; thanks to Yuan Kang, I now have a copy of the data.

The plots below (code+data) show two representations of the non-error/error path lengths (measured in statements within individual functions of libc; counting starts at a library call that could return an error value). The upper plot shows statement sequence lengths for error/non-error paths, and the lower is a kernel density plot of the error/non-error sequence lengths.

Another truism is that people tend to write positive tests, i.e., tests that do not involve error handling (some evidence).

Code coverage measurements (e.g., number of statements or branches that are executed by a test suite) often show the pattern seen in the plot below (code+data; thanks to the authors of the paper Code Coverage for Suite Evaluation by Developers for making the data available). The data was obtained by measuring the coverage of 1,043 Java programs executing their associated test suite (circles denote program size). Lines are fitted regression models for different sized programs.

If people are preferentially writing positive tests, test suites with low coverage would be expected to execute a greater percentage of statements than branches (an if-statement has two branches, taken/not-taken), i.e., the behavior seen in the plot above (grey line shows equal statement/branch coverage). Once the low hanging fruit is tested (i.e., the longer, non-error, cases), tests have to be written for the shorter, more likely to be error handling, cases.

The plot would also be explained by typical execution paths favoring longer basic blocks, but I don’t have any data that could show this one way or another.

## Mathematical proofs contain faults, just like software

The idea of proving programs correct, like mathematical proofs, is appealing, but is based on an incorrect assumption often made by non-mathematicians, e.g., mathematical proofs are fault free. In practice, mathematicians make mistakes and create proofs that contain serious errors; those of us who are taught mathematical techniques, but are not mathematicians, only get to see the good stuff that has been checked over many years.

An appreciation that published proofs contain mistakes is starting to grow, but Magnificent mistakes in mathematics is an odd choice for a book title on the topic. Quotes from De Millo’s article on “Social Processes and Proofs of Theorems and Programs” now appear regularly; On proof and progress in mathematics is worth a read.

Are there patterns to the faults that appear in claimed mathematical proofs?

A surprisingly common approach, used by mathematicians to avoid faults in their proofs, is to state theorems without giving a formal proof (giving an informal one is given instead). There are plenty of mathematicians who don’t think proofs are a big part of mathematics (various papers from the linked-to book are available as pdfs).

Next time you encounter an advocate of proving programs correct using mathematics, ask them what they think about the uncertainty about claimed mathematical proofs and all the mistakes that have been found in published proofs.

## Compiler validation is now part of history

Compiler validation makes sense in a world where there are many different hardware platforms, each with their own independent compilers (third parties often implemented compilers for popular platforms, competing against the hardware vendor). A large organization that spends hundreds of millions on a multitude of computer systems (e.g., the U.S. government) wants to keep prices down, which means the cost of porting its software to different platforms needs to be kept down (or at least suppliers need to think it will not cost too much to switch hardware).

A crucial requirement for source code portability is that different compilers be able to compile the same source, generating code that produces the same behavior. The same behavior requirement is an issue when the underlying word-size varies or has different alignment requirements (lots of code relies on data structures following particular patterns of behavior), but management on all sides always seems to think that being able to compile the source is enough. Compilers vendors often supported extensions to the language standard, and developers got to learn they were extensions when porting to a different compiler.

The U.S. government funded a conformance testing service, and paid for compiler validation suites to be written (source code for what were once the Cobol 85, Fortran 78 and
SQL validation suites). While it was in business, this conformance testing service was involved C compiler validation, but it did not have to fund any development because commercial test suites were available.

The 1990s was the mass-extinction decade for companies selling non-Intel hardware. The widespread use of Open source compilers, coupled with the disappearance of lots of different cpus (porting compilers to new vendor cpus was always a good money spinner, for the compiler writing cottage industry), meant that many compilers disappeared from the market.

These days, language portability issues have been essentially solved by a near mono-culture of compilers and cpus. It’s the libraries that are the primary cause of application portability problems. There is a test suite for POSIX and Linux has its own tests.

There are companies selling compiler C/C++ test suites (e.g., Perennial and PlumHall); when maintaining a compiler it’s cost effective to have a set of third-party tests designed to exercise all the language.

The OpenGroup offer to test your C compiler and issue a brand certificate if it passes the tests.

Source code portability requires compilers to have the same behavior and traditionally the generally accepted behavior has been defined by an ISO Standard or how one particular implementation behaved. In an Open source world behavior is defined by what needs to be done to run the majority of existing code. Does it matter if Open source compilers evolve in a direction that is different from the behavior specified in an ISO Standard? I think not, it makes no difference to the majority of developers; but be careful, saying this can quickly generate a major storm in a tiny teacup.

## What instructions should a computer support?

The modern answer to what instructions should a computer support is: lots (e.g., many kinds of: add, subtract, compare, branch, load, store, etc). John von Neumann’s famous First Draft of a Report on the EDVAC, written in 1945, specifies 97 instructions (later, actual implementations contained fewer instructions) and modern Intel processors contain several thousand instructions.

The Turing machine has a very simple instruction set; the machine is driven by a lookup table that specifies one or more of the operations: erase/write a symbol (to the cell currently under the read/write head), move the read/write head left or right (on a tape containing cells), and load a new state (which may be the same as the current one).

When computers were new, the lure of creating a minimalist instruction set had theoretical and practical appeal (valve computers were large and unreliable, reducing the number of components improved reliability and reduced costs).

The design of the IAS machine, built in the late 1940s, was based on von Neumann’s design. Haskell Curry came up with a minimalist set of four instructions that could be used to implement its supported instructions (the idea was that programs would be stored in minimalist form to reduce storage overheads).

Minimalist instruction sets still have theoretical appeal.

Simplicity (rather than minimalism) became fashionable in the 1980s with RISC. This was a reaction to the implementation/runtime costs of very complicated of instructions found in DEC’s VAX and later Motorola’s 68000 processors. Supporting these complicated instructions generated additional overhead for the simpler instructions (which is what most programs spent most of their time executing). The idea behind RISC was that simplifying the instruction set would reduce cpu design costs, improve performance (by making simple instructions fast); leaving the complicated stuff to be supported via software.

Starting out with ‘simple’ MIPS, RISC cpus got successively more complicated with SPARC, Motorola’s MC88000 and then IBM’s RS/6000. I worked on code generators for the SPARC and MC88000 and found them somewhat dull after working on CISC processors. There were huge arguments around RISC vs. CISC (I suspect that many of those involved had never used a RISC processor), but then this was back in the days when many programmers knew a lot of the technical details about the processors they used. (How many of today’s programmers can name the Intel x86 registers?)

More background on 1950s minimalism in the paper: Less is more in the Fifties. Encounters between Logical Minimalism and Computer Design during the 1950s.

These days people are inventing very different architectures within which existing instructions have to operate, rather than radically new instructions.

## Learning a cpu’s instruction set

A few years ago I wrote about the possibility of secret instruction sets making a comeback and the minimum information needed to write a code generator. A paper from the sporadic (i.e., they don’t release umpteen slices of the same overall paper), but always interesting, group at Stanford describes a tool that goes a long way to solving the secret instruction set problem; stratified synthesis learns an instruction set, starting from a small set of known instructions.

After feeding in 51 base instructions and 11 templates, 1,795.42 instruction ‘formulas’ were learned (119.42 were 8-bit constant instructions, every variant counted as 1/256 of an instruction); out of a maximum of 3,683 possible instructions (depending on how you count instructions).

As well as discovering ‘new’ instructions, they also discovered bugs in the Intel 64 and IA-32 Architectures Software Developer Manuals. In my compiler writing days, bugs in cpu documentation were a pet hate (they cause huge amounts of time to be wasted).

The initial starting information used is rather large, from the perspective of understanding the instruction set of an unknown cpu. I’m sure others will be working to reduce the necessary startup information needed to obtain useful results. The Intel Management Engine is an obvious candidate for investigation.

Vendors sometimes add support for instructions without publicizing them and sometimes certain bit patterns happen to do something sensible in a particular version of a design because some random pattern of bits happens to do whatever it does without being treated as an illegal opcode. You journey down the rabbit hole starts here.

On a related note, I continue to be amazed that widely used disassemblers fail to correctly handle surprisingly many, documented, x86 opcodes; benchmarks from 2010 and 2016

## First use of: software, software engineering and source code

While reading some software related books/reports/articles written during the 1950s, I suddenly realized that the word ‘software’ was not being used. This set me off looking for the earliest use of various computer terms.

My search process consisted of using pfgrep on my collection of pdfs of documents from the 1950s and 60s, and looking in the index of the few old computer books I still have.

Software: The Oxford English Dictionary (OED) cites an article by John Tukey published in the American Mathematical Monthly during 1958 as the first published use of software: “The ‘software’ comprising … interpretive routines, compilers, and other aspects of automotive programming are at least as important to the modern electronic calculator as its ‘hardware’.”

I have a copy of the second edition of “An Introduction to Automatic Computers” by Ned Chapin, published in 1963, which does a great job of defining the various kinds of software. Earlier editions were published in 1955 and 1957. Did these earlier edition also contain various definitions of software? I cannot find any reasonably prices copies on the second-hand book market. Do any readers have a copy?

Software engineering: The OED cites a 1966 “letter to the ACM membership” by Anthony A. Oettinger, then ACM President: “We must recognize ourselves … as members of an engineering profession, be it hardware engineering or software engineering.”

The June 1965 issue of COMPUTERS and AUTOMATION, in its Roster of organizations in the computer field, has the list of services offered by Abacus Information Management Co.: “systems software engineering”, and by Halbrecht Associates, Inc.: “software engineering”. This pushes the first use of software engineering back by a year.

Source code: The OED cites a 1965 issue of Communications ACM: “The PUFFT source language listing provides a cross reference between the source code and the object code.”

The December 1959 Proceedings of the EASTERN JOINT COMPUTER CONFERENCE contains the article: “SIMCOM – The Simulator Compiler” by Thomas G. Sanborn. On page 140 we have: “The compiler uses this convention to aid in distinguishing between SIMCOM statements and SCAT instructions which may be included in the source code.”

Running pdfgrep over the archive of documents on bitsavers would probably throw up all manners of early users of software related terms.

I have been reading two very different computer books written for a general readership: Giant Brains or Machines that Think, published in 1949 (with a retrospective chapter added in 1961) and LET ERMA DO IT, published in 1956.

‘Giant Brains’ by Edmund Berkeley, was very popular in its day.

Berkeley marvels at a computer performing 5,000 additions per second; performing all the calculations in a week that previously required 500 human computers (i.e., people using mechanical adding machines) working 40 hours per week. His mind staggers at the “calculating circuits being developed” that can perform 100,00 additions a second; “A mechanical brain that can do 10,000 additions a second can very easily finish almost all its work at once.”

The chapter discussing the future, “Machines that think, and what they might do for men”, sees Berkeley struggling for non-mathematical applications; a common problem with all new inventions. Automatic translator and automatic stenographer (typist who transcribe dictation) are listed. There is also a chapter on social control, which is just as applicable today.

This was the first widely read book to promote Shannon‘s idea of using the algebra invented by George Boole to analyze switching circuits symbolically (THE 1940 Masters thesis).

The ‘ERMA’ book paints a very rosy picture of the future with computer automation removing the drudgery that so many jobs require; it is so upbeat. A year later the USSR launched Sputnik and things suddenly looked a lot less rosy.

## Was a C90, C99, or C11 compiler used?

How can a program figure out whether it has been compiled with a C90, C99 or C11 compiler?

Support for the `//` style of commenting was added in C99.

Support for Unicode string literals (e.g., `U"Hello World"`) was added in C11.

Putting these together we get the following:

```#include <stdio.h>   #define M(U) sizeof(U"s"[0])   int main(void) { switch(M("")*2 //**/ 2 ) { case 1: printf("C90\n"); break; case 2: printf("C99\n"); break; case 8: printf("C11\n"); break; }   }```

## Almost all published analysis of fault data is worthless

Faults are the subject of more published papers that any other subject in empirical software engineering. Unfortunately, over 98.5% of these fault related papers are at best worthless and at worst harmful, i.e., make recommendations whose impact may increase the number of faults.

The reason most fault papers are worthless is the data they use and the data they don’t to use.

The data used

Data on faults in programs used to be hard to obtain, a friend in a company that maintained a fault database was needed. Open source changed this. Now public fault tracking systems are available containing tens, or even hundreds, of thousands of reported faults. Anybody can report a fault, and unfortunately anybody does; there is a lot of noise mixed in with the signal. One study found 43% of reported faults were enhancement requests, the same underlying fault is reported multiple times (most eventually get marked as duplicate, at the cost of much wasted time) and …

Fault tracking systems don’t always contain all known faults. One study found that the really important faults are handled via email discussion lists, i.e., they are important enough to require involving people directly.

Other problems with fault data include: biased reported of problems, reported problem caused by a fault in a third-party library, and reported problem being intermittent or not reproducible.

Data cleaning is the essential first step that many of those who analyze fault data fail to perform.

The data not used

Users cause faults, i.e., if nobody ever used the software, no faults would be reported. This statement is as accurate as saying: “Source code causes faults”.

Reported faults are the result of software being used with a set of inputs that causes the execution of some sequence of tokens in the source code to have an effect that was not intended.

The number and kind of reported faults in a program depends on the variety of the input and the number of faults in the code.

Most fault related studies do not include any user related usage data in their analysis (the few that do really stand out from the crowd), which can lead to very wrong conclusions being drawn.

User usage data is very hard to obtain, but without it many kinds of evidence-based fault analysis are doomed to fail (giving completely misleading answers).

## The first compiler was implemented in itself

I have been reading about the world’s first actual compiler (i.e., not a paper exercise), described in Corrado Böhm’s PhD thesis (French version from 1954, an English translation by Peter Sestoft). The thesis, submitted in 1951 to the Federal Technical University in Zurich, takes some untangling; when you are inventing a new field, ideas tend to be expressed using existing concepts and terminology, e.g., computer peripherals are called organs and registers are denoted by the symbol .

Böhm had work with Konrad Zuse and must have known about his language, Plankalkül. The language also has a APL feel to it (but without the vector operations).

Böhm’s language does not have a name, his thesis is really about translating mathematical expressions to machine code; the expressions are organised by what we today call basic blocks (Böhm calls them groups). The compiler for the unnamed language (along with a loader) is written in itself; a Java implementation is being worked on.

Böhm’s work is discussed in Donald Knuth’s early development of programming languages, but there is nothing like reading the actual work (if only in translation) to get a feel for it.

## The shadow of the input distribution

Two things need to occur for a user to experience a fault in a program:

• a fault has to exist in the code,
• the user has to provide input that causes program execution to include the faulty code in a way that exhibits the incorrect behavior.

Data on the distribution of user input values is extremely rare, and we are left having to look for the shadows that the input distribution creates.

Csmith is a well-known tool for generating random C source code. I spotted an interesting plot in a compiler fuzzing paper and Yang Chen kindly sent me a copy of the data. In compiler fuzzing, source code is automatically generated and fed to the compiler, various techniques are used to figure out when the compiler gets things wrong.

The plot below is a count of the number of times each fault in gcc has been triggered (code+data). Multiple occurrences of the same fault are experienced because the necessary input values occur multiple times in the generated source code (usually in different files).

The green line is a fitted regression model, it’s a bi-exponential, i.e., the sum of two exponentials (the straight lines in red and blue).

The obvious explanation for this bi-exponential behavior (explanations invented after seeing the data can have the flavor of just-so stories, which is patently not true here is that one exponential is driven by the presence of faults in the code and the other exponential is driven by the way in which Csmith meanders over the possible C source.

So, which exponential is generated by the faults and which by Csmith? I’m still trying to figure this out; suggestions welcome, along with alternative explanations.

Is the same pattern seen in duplicates of user reported faults? It does in the small amount of data I have; more data welcome.

## Christmas books for 2017

Some suggestions for books this Christmas. As always, the timing of books I suggest is based on when they reach the top of my books-to-read pile, not when they were published.

“Life ascending: The ten great inventions of evolution” by Nick Lane. The latest thinking (as of 2010) on the major events in the evolution of life. Full of technical detail, very readable, and full of surprises (at least for me).

“How buildings learn” by Stewart Brand. Yes, I’m very late on this one. So building are just like software, people want to change them in ways not planned by their builders, they get put to all kinds of unexpected uses, some of them cannot keep up and get thrown away and rebuilt, while others age gracefully.

“Dead Man Working” by Cederström and Fleming is short and to the point (having an impact on me earlier in the year), while “No-Collar: The humane workplace and its hidden costs” by Andrew Ross is longer (first half is general, second a specific instance involving one company). Both have a coherent view work in the knowledge economy.

If you are into technical books on the knowledge economy, have a look at “Capitalism without capital” by Haskel and Westlake (the second half meanders off, covering alleged social consequences), and “Antitrust law in the new economy” by Mark R. Patterson (existing antitrust thinking is having a very hard time grappling with knowledge-based companies).

If you are into linguistics, then “Constraints on numerical expressions” by Chris Cummins (his PhD thesis is free) provides insight into implicit assumptions contained within numerical expressions (of the human conversation kind). A must read for anybody interested in automated fact checking.

## ISO/IEC JTC 1/SC 42 Artificial intelligence

What has been preventing Artificial Intelligence being a success? Yes, you guessed it, until now ISO has not had an SC (Standards’ Committee) dealing with AI. Well, the votes are in and JTC 1/SC 42 Artificial intelligence is go.

Countries pay ISO to be members of an SC and the tax payers of: Austria, Canada, Finland, Germany, Ireland, Switzerland and United States have the pleasure of being founding member countries of SC42.

What standards/technical-reports are those attending SC42 meetings going to working on?

The two document titles I have seen so far are: “Artificial Intelligence Concepts and Terminology” and “Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)”.

I hope the terminology document arrives in plenty of time, before the machines take over. The ISO Standard for Year 2000 terminology arrived in December 1999 (there was a flurry of emails desperately trying to row-back on this document).

Want to join up? Wael William Diab is the chairperson.

## Vanity project or real research?

I gave a talk at the CREST Open Workshop yesterday. Many of those attending and speaking were involved in empirical work of one kind or another. My experience is that researchers involved in empirical work, in software engineering, feel the need to justify using this approach to research, because it is different from what many others in the field do. I want to reverse this perception; those not doing empirical work are the ones that should feel the need to justify their approach to research.

Evidence obtained from experiments and measurements are the basis of the scientific method.

I started by contrasting a typical software engineering researcher’s view of their work (both images from Wikipedia under a Creative Commons Attribution-ShareAlike License):

with a common industry view of academic researchers:

The reputation of those doing evidence based research is being completely overshadowed by those who use ego and bluster to promote their claims. We needed an effective label for work that is promoted using ego and bluster, and I proposed vanity projects (the work done by the ego and bluster crowd does not deserve to be referred to as research). Yes, there are plenty of snake-oil salesmen in industry, but that is another issue.

Vanity projects being passed off as research should be named and shamed.

Next time you are in the audience listening to claims made by the speaker about the results of his/her research, that are not backed up by experiments or measurements, ask them why decided to pursue a vanity project rather than proper research.

## Data-set update to “Empirical software engineering using R”

The pile of papers, books and data-sets, relating to previously released draft chapters of my Empirical software engineering book, has been growing, and cluttering up my mind. I decided to have a clear-out.

A couple of things stood out.

There are around 25 data-sets that have been promised but not yet arrived. If you encounter anybody who mentions they promised to send me data, please encourage them to spend some time doing this. I don’t want to add a new category, promised but never delivered, to the list of email responses.

There has been an increase in data-sets not being used because I already have something better. This is a good sign, data quality is increasing. One consequence is that a growing number of ‘historical’ data-sets have fallen by the wayside. This is a good thing, most data-sets analysed in papers are very low quality and only used because nothing else was available.

One of my reasons for making draft releases was to prompt people to suggest data I had missed. This has not happened yet; come on people, suggest some data I don’t yet know about.

About a third of the pile got included in the latest draft, a third had been superseded by something better, and a third are still waiting for promised data.

Now, back to the reliability chapter.

## Grace Hopper: Manager, after briefly being a programmer

In popular mythology Grace Hopper is a programmer who wrote one of the first compilers. I think the reality is that Hopper did some programming, but quickly moved into management; a common career path for freshly minted PhDs and older people entering computing (Hopper was in her 40s when she started); her compiler management work occurred well after many other compilers had been written.

What is the evidence?

Hopper is closely associated with Cobol. There is a lot of evidence for at least 28 compilers in 1957, well before the first Cobol compiler (can a compiler written after the first 28 be called one of the first?)

The A-0 tool, which Hopper worked on as a programmer in 1951-52, has been called a compiler. However, the definition of Compile used sounds like today’s assembler and the definition of Assemble used sounds like today’s link-loader (also see: section 7 of Digital Computers – Advanced Coding Techniques for Hopper’s description of what A-2, a later version, did).

The ACM’s First Glossary of Programming Terminology, produced by a committee chaired by Hopper in June 1954.

Routine – a set of coded instructions arranged in proper sequence to direct the computer to perform a desired operation or series of operations. See also Subroutine.

Compiler (Compiling Routine) – an executive routine which, before the desired computation is started, translates a program expressed in pseudo-code into machine code (or into another pseudo-code for further translation by an interpreter). In accomplishing the translation, the compiler may be required to:

Assemble – to integrate the subroutines (supplied, selected, or generated) into the main routine, i.e., to:

Adapt – to specialize to the task at hand by means of preset parameters.

Orient – to change relative and symbolic addresses to absolute form.

Incorporate – to place in storage.

Hopper’s name is associated with work on the MATH-MATIC and ARITH-MATIC Systems, but her name does not appear in the list of people who wrote the manual in 1957. A programmer working on these systems is likely to have been involved in producing the manual.

After the A-0 work, all of Hopper’s papers relate to talks she gave, committees she sat on and teams she led, i.e., the profile of a manager.

## À la carte Entropy

My observation that academics treat Entropy as the go-to topic, when they have no idea what else to talk about, has ruffled a few feathers. David Clark, one of the organizers of a workshop on Information Theory and Software Testing has invited me to give a talk on Entropy (the title is currently Entropy for the uncertain, but this state might change :-).

Complaining about the many ways entropy is currently misused in software engineering would be like shooting fish in a barrel, and equally pointless. I want to encourage people to use entropy in a meaningful way, and to stop using Shannon entropy just because it is the premium brand of entropy.

Shannon’s derivation of the iconic formula depends on various assumptions being true. While these conditions look like they might hold for some software engineering problems, they clearly don’t hold for others. It may be possible to use other forms of entropy for some of these other problems; Shannon became the premium brand of entropy because it was first to market, the other entropy products have not had anyone championing their use, and academics follow each other like sheep (it’s much easier to get a paper published by using the well-known brands).

Shannon’s entropy has been generalized, with the two most well-known being (in the limit , both converge to Shannon entropy):

Rényi entropy in 1961:

Tsallis entropy in 1988:

All of these formula reduce a list of probabilities to a single value. A weighting is applied to each probability, and this weighted value is summed to produce a single value that is further manipulated. The probability weighting functions are plotted below:

Under what conditions might one these two forms of entropy be used (there other forms)? I have been rummaging around looking for example uses, and could not find many.

There are some interesting papers about possible interpretations of the parameter in Tsallis entropy: the most interesting paper I have found shows a connection with the correlation between states, e.g., preferential attachment in networks. This implies that Tsallis entropy is the natural first candidate to consider for systems exhibiting power law characteristics. Another paper suggests derives from variation in the parameter of an exponential equation.

Some computer applications: a discussion of Tsallis entropy and the concept of non-extensive entropy, along with an analysis of statistical properties of hard disc workloads, the same idea applied to computer memory.

Some PhD thesis: Rényi entropy, with , for error propagation in software architectures, comparing various measures of entropy as a metric for the similarity of program execution traces, plus using Rényi entropy in cryptography

As you can see, I don’t have much to talk about. I’m hoping my knowledgeable readers can point me at some uses of entropy in software engineering where the author has put some thought into which entropy to use (which may have resulted in Shannon entropy being chosen; I’m only against this choice when it is made for brand name reasons).

Registration for the workshop is open, so turn up and cheer me on.

```p_vals=seq(0.001, 1.001, by=0.01) plot(p_vals, -p_vals*log(p_vals), type="l", col="red", ylim=c(0, 1), xaxs="i", yaxs="i", xlab="Probability", ylab="Weight")   q=0.5 lines(p_vals, p_vals^q, type="l", col="blue") q=2 lines(p_vals, p_vals^q, type="l", col="green")```

## Double exponential performance hit in C# compiler

Yesterday I was reading a blog post on the performance impact of using duplicated nested types in a C# generic class, such as the following:

```class Class<A, B, C, D, E, F> { class Inner : Class<Inner, Inner, Inner, Inner, Inner, Inner> { Inner.Inner inner; } }```

Ah, the joys of exploiting a language specification to create perverse code

The author, Matt Warren, had benchmarked the C# compiler for increasing numbers of duplicate types; good man. However, the exponential fitted to the data, in the blog post, is obviously not a good fit. I suspected a double exponential might be a better fit and gave it a go (code+data).

Compile-time data points and fitted double-exponential below:

Yes, for compile time the better fit is: and for binary size: , where , are some constants and the number of duplicates.

I had no idea what might cause this pathological compiler performance problem (apart from the pathological code) and did some rummaging around. No luck; I could not find any analysis of generic type implementation that might be used to shine some light on this behavior.

Ideas anybody?

## Huge effort data-set for project phases

I am becoming a regular reader of software engineering articles written in Chinese and Japanese; or to be more exact, I am starting to regularly page through pdfs looking at figures and tables of numbers, every now and again cutting-and-pasting sequences of logograms into Google translate.

A few weeks ago I saw the figure below, and almost fell off my chair; it’s from a paper by Yong Wang and Jing Zhang. These plots are based on data that is roughly an order of magnitude larger than the combined total of all the public data currently available on effort break-down by project phase.

Projects are often broken down into phases, e.g., requirements, design, coding (listed as ‘produce’ above), testing (listed as ‘optimize’), deployment (listed as ‘implement’), and managers are interested in knowing what percentage of a project’s budget is typically spent on each phase.

Projects that are safety-critical tend to have high percentage spends in the requirements and testing phase, while in fast moving markets resources tend to be invested more heavily in coding and deployment.

Research papers on project effort usually use data from earlier papers. The small number of papers that provide their own data might list effort break-down for half-a-dozen projects, a few require readers to take their shoes and socks off to count, a small number go higher (one from the Rome period), but none get into three-digits. I have maybe a few hundred such project phase effort numbers.

I emailed the first author and around a week later had 2,570 project phase effort (man-hours) percentages (his co-author was on marriage leave, which sounded a lot more important than my data request); see plot below (code+data).

I have tried to fit some of the obvious candidate distributions to each phase, but none of the fits were consistently good across the phases (see code for details).

This project phase data is from small projects, i.e., one person over a few months to ten’ish people over more than a year (a guess based on the total effort seen in other plots in the paper).

A typical problem with samples in software engineering is their small size (apart from bugs data, lots of that is available, at least in uncleaned form). Having a sample of this size means that it should be possible to have a reasonable level of confidence in the results of statistical tests. Now we just need to figure out some interesting questions to ask.

## Projects chapter added to “Empirical software engineering using R”

The Projects chapter of my Empirical software engineering book has been added to the draft pdf (download here).

This material turned out to be harder to bring together than I had expected.

Building software projects is a bit like making sausages in that you don’t want to know the details, or in this case those involved are not overly keen to reveal the data.

There are lots of papers on requirements, but remarkably little data (Soo Ling Lim’s work being the main exception).

There are lots of papers on effort prediction, but they tend to rehash the same data and the quality of research is poor (i.e., tweaking equations to get a better fit; no explanation of why the tweaks might have any connection to reality). I had not realised that Norden did all the heavy lifting on what is sometimes called the Putnam model; Putnam was essentially an evangelist. The Parr curve is a better model (sorry, no pdf), but lacked an evangelist.

Accurate estimates are unrealistic: lots of variation between different people and development groups, the client keeps changing the requirements and developer turnover is high.

I did turn up a few interesting data-sets and Rome came to the rescue in places.

I have been promised more data and am optimistic some will arrive.

As always, if you know of any interesting software engineering data, please tell me.

I’m looking to rerun the workshop on analyzing software engineering data. If anybody has a venue in central London, that holds 30 or so people+projector, and is willing to make it available at no charge for a series of free workshops over several Saturdays, please get in touch.

Reliability chapter next.

## Is this fitted line believable? A visual answer

The only information contained in the statement that a straight line has been fitted to the data, is that the data contains two or more points; modern tools will find a fit to anything that is thrown at them, without raising a sweat; a quadratic equation requires three or more points and so on.

How believable is an equation that has been fitted to data?

There are various technical ways of answering this question, but as a first pass I prefer a simple visual approach. How believable do the lines in the plots below appear to you (code+data)?

Now I could fire p-values at you, or show you various regression diagnostic plots. Would you be any the wiser? If you were it’s because you know some technical details and switched your brain on to use them. People hate having to switch their brain’s on; a technique that works with the brain switched off is much more practical.

Adding confidence intervals to a plot is one technique (below left uses the default 95% interval) and another is to draw the line of a LOESS fit (below right uses R’s loess function):

The confidence intervals (in blue) are showing us that there is huge uncertainty in the fitted equation; no technical details needed.

The Local in LOESS means that some local set of points are used to fit each part of the line. That green line is telling us that the mean value of the data does not continue to increase, but levels off (the data is from a NASA presentation that showed an ever-increasing fitted line; which is the view the speaker wanted people to believe).

If somebody shows you a line that has been fitted to the data, ask to see the confidence intervals and the loess from a fit. The willingness of the person to show you these, or their ability to do so, will tell you a lot.

## Data analysis with a manual mindset

A lot of software engineering data continues to be analysed using techniques designed for manual implementation (i.e., executed without a computer). Yes, these days computers are being used to do the calculation, but they are being used to replicate the manual steps.

Statistical techniques are often available that are more powerful than the ‘manual’ techniques. They were not used during the manual-era because they are too computationally expensive to be done manually, or had not been invented yet; the bootstrap springs to mind.

What is the advantage of these needs-a-computer techniques?

The main advantage is not requiring that the data have a Normal distribution. While data having a Normal, or normal-like, distribution is common is the social sciences (a big consumer of statistical analysis), it is less common in software engineering. Software engineering data is often skewed (at least the data I have analysed) and what appear to be outliers are common.

It seems like every empirical paper I read uses a Mann-Whitney test or Wilcoxon signed-rank test to compare two samples, sometimes preceded by a statement that the data is close to being Normal, more often being silent on this topic, and occasionally putting some effort into showing the data is Normal or removing outliers to bring it closer to being Normally distributed.

Why not use a bootstrap technique and not have to bother about what distribution the data has?

I’m not sure whether the reason is lack of knowledge about the bootstrap or lack of confidence in not following the herd (i.e., what will everybody say if my paper does not use the techniques that everybody else uses?)

If you are living on a desert island and don’t have a computer, then you will want to use the manual techniques. But then you probably won’t be interested in analyzing software engineering data.

## Histogram using log scale creates a visual artifact

The following plot appears in the paper Stack Overflow in Github: Any Snippets There?

Don’t those twin peaks in the top-left/bottom-right plots reach out and grab your attention? I immediately thought of fitting a mixture of two Poisson distributions; No, No, No, something wrong here. The first question of data analysis is: Do I believe the data?

The possibility of fake data does not get asked until more likely possibilities have been examined first.

The y-axis is a count of things and the x-axis shows the things being counted; source files per project and functions per file, in this case.

All the measurements I know of show a decreasing trend for these things, e.g., lots of projects have a few files and a few projects have lots of files. Twin peaks is very unexpected.

I have serious problems believing this data, because it does not conform to my prior experience. What have the authors done wrong?

My first thought was that a measuring mistake had been made; for some reason values over a certain range were being incorrectly miscounted.

Then I saw the problem. The plot was of a histogram and the x-axis had a logarithmic scale. A logarithmic axis compresses the range in a non-linear fashion, which means that variable width bins have to be used for histograms and the y-axis represents density (not a count).

Taking logs and using the result to plot a histogram usually produces a curve having a distorted shape, not twin peaks. I think the twin peaks occur here because integer data are involved and the bin width just happened to have the ‘right’ value.

Looking at the plot below, the first bin contains values for `x=1` (on an un-logged scale), the second bin for `x=2`, the third bin for `x=3`, but the fourth bin contains values for `x=c(4, 5, 6)`. The nonlinear logarithmic compression, mapped to integers, means that the contents of three values are added to a single bin, creating a total that is larger than the third bin.

The R code that generated the above plot:

```x=1:1e6 y=trunc(1e6/x^1.5) log_y=log10(y)   hist(log_y, n=40, main="", xlim=c(0, 3))```

I tried to mimic the pattern seen in the first histogram by trying various exponents of a power law (a common candidate for this kind of measurement), but couldn’t get anything to work.

Change the bin width can make the second peak disappear, or rather get smeared out. Still a useful pattern to look out for in the future.

## Expected variability in a program’s SLOC

If 10 people independently implement the same specification in the same language, how much variation will there be in the length of their programs (measured in lines of code)?

The data I have suggests that the standard deviation of program length is one quarter of the mean length, e.g., 10k mean length, 2.5k standard deviation.

The plot below (code+data) shows six points from the samples I have. The point in the bottom left is based on 6,300 C programs from a programming contest question requiring solutions to the 3n+1 problem and one of the points on the right comes from five Pascal compilers for the same processor.

Multiple implementations of the same specification, in the same language, are very rare. If you know of any, please let me know.

## Experimental method for measuring benefits of identifier naming

I was recently came across a very interesting experiment in Eran Avidan’s Master’s thesis. Regular readers will know of my interest in identifiers; while everybody agrees that identifier names have a significant impact on the effort needed to understand code, reliably measuring this impact has proven to be very difficult.

The experimental method looked like it would have some impact on subject performance, but I was not expecting a huge impact. Avidan’s advisor was Dror Feitelson, who kindly provided the experimental data, answered my questions and provided useful background information (Dror is also very interested in empirical work and provides a pdf of his book+data on workload modeling).

Avidan’s asked subjects to figure out what a particular method did, timing how long it took for them to work this out. In the control condition a subject saw the original method and in the experimental condition the method name was replaced by `xxx` and local and parameter names were replaced by single letter identifiers. The hypothesis was that subjects would take longer for methods modified to use ‘random’ identifier names.

A wonderfully simple idea that does not involve a lot of experimental overhead and ought to be runnable under a wide variety of conditions, plus the difference in performance is very noticeable.

The think aloud protocol was used, i.e., subjects were asked to speak their thoughts as they processed the code. Having to do this will slow people down, but has the advantage of helping to ensure that a subject really does understand the code. An overall slower response time is not important because we are interested in differences in performance.

Each of the nine subjects sequentially processed six methods, with the methods randomly assigned as controls or experimental treatments (of which there were two, locals first and parameters first).

The procedure, when a subject saw a modified method was as follows: the subject was asked to explain the method’s purpose, once an answer was given either the local or parameter names were revealed and the subject had to again explain the method’s purpose, and when an answer was given the names of both locals and parameters was revealed and a final answer recorded. The time taken for the subject to give a correct answer was recorded.

The `summary` output of a model fitted using a mixed-effects model is at the end of this post (code+data; original experimental materials). There are only enough measurements to have `subject` as a random effect on the `treatment`; no order of presentation data is available to look for learning effects.

Subjects took longer for modified methods. When parameters were revealed first, subjects were 268 seconds slower (on average), and when locals were revealed first 342 seconds slower (the standard deviation of the between subject differences was 187 and 253 seconds, respectively; less than the treatment effect, surprising, perhaps a consequence of information being progressively revealed helping the slower performers).

Why is subject performance less slow when parameter names are revealed first? My thoughts: parameter names (if well-chosen) provide clues about what incoming values represent, useful information for figuring out what a method does. Locals are somewhat self-referential in that they hold local information, often derived from parameters as initial values.

What other factors could impact subject performance?

The number of occurrences of each name in the body of the method provides an opportunity to deduce information; so I think time to figure out what the method does should less when there are many uses of locals/parameters, compared to when there are few.

The ability of subjects to recognize what the code does is also important, i.e., subject code reading experience.

There are lots of interesting possibilities that can be investigated using this low cost technique.

```Linear mixed model fit by REML ['lmerMod'] Formula: response ~ func + treatment + (treatment | subject) Data: idxx   REML criterion at convergence: 537.8   Scaled residuals: Min 1Q Median 3Q Max -1.34985 -0.56113 -0.05058 0.60747 2.15960   Random effects: Groups Name Variance Std.Dev. Corr subject (Intercept) 38748 196.8 treatmentlocals first 64163 253.3 -0.96 treatmentparameters first 34810 186.6 -1.00 0.95 Residual 43187 207.8 Number of obs: 46, groups: subject, 9   Fixed effects: Estimate Std. Error t value (Intercept) 799.0 110.2 7.248 funcindexOfAny -254.9 126.7 -2.011 funcrepeat -560.1 135.6 -4.132 funcreplaceChars -397.6 126.6 -3.140 funcreverse -466.7 123.5 -3.779 funcsubstringBetween -145.8 125.8 -1.159 treatmentlocals first 342.5 124.8 2.745 treatmentparameters first 267.8 106.0 2.525   Correlation of Fixed Effects: (Intr) fncnOA fncrpt fncrpC fncrvr fncsbB trtmntlf fncndxOfAny -0.524 funcrepeat -0.490 0.613 fncrplcChrs -0.526 0.657 0.620 funcreverse -0.510 0.651 0.638 0.656 fncsbstrngB -0.523 0.655 0.607 0.655 0.648 trtmntlclsf -0.505 -0.167 -0.182 -0.160 -0.212 -0.128 trtmntprmtf -0.495 -0.184 -0.162 -0.184 -0.228 -0.213 0.673```

## An Almanac of the Internet

My search for software engineering data has turned me into a frequent buyer of second-hand computer books, many costing less than the postage of £2.80. When the following suggestion popped up along-side a search, I could not resist; there must be numbers in there!

The concept of an Almanac will probably be a weird idea to readers who grew up with search engines and Wikipedia. But yes, many years ago, people really did make a living by manually collecting information and selling it in printed form.

One advantage of the printed form is that updating it requires a new copy, the old copy lives on unchanged (unlike web pages); the disadvantage is taking up physical space (one day I will probably abandon this book in a British rail coffee shop).

Where did Internet users hang out in 1997?

The history of the Internet, as it appeared in 1997.

Of course, a list of web sites is an essential component of an Internet Almanac:

## Investing in the gcc C++ front-end

I recently found out that RedHat are investing in improving the C++ front-end of gcc, i.e., management have assigned developers to work in this area. What’s in it for RedHat? I’m told there are large companies (financial institutions feature) who think that using some of the features added to recent C++ standards (these have been appearing on a regular basis) will improve the productivity of their developers. So, RedHat are hoping this work will boost their reputation and increase their sales to these large companies. As an ex-compiler guy (ex- in the sense of being promoted to higher levels that require I don’t do anything useful), I am always in favor or companies paying people to work on compilers; go RedHat.

Is there any evidence that features that have been added to any programming language improved developer productivity? The catch to this question is defining programmer productivity. There have been several studies showing that if productivity is defined as number of assembly language lines written per day, then high level languages are more productive than assembler (the lines of assembler generated by the compiler were counted, which is rather compiler dependent).

Of the 327 commits made this year to the gcc C++ front-end, by 29 different people, 295 were made by one of 17 people employed by RedHat (over half of these commits were made by two people and there is a long tail; nine people each made less than four commits). Measuring productivity by commit counts has plenty of flaws, but has the advantage of being easy to do (thanks Jonathan).

## The fuzzy line between reworking and enhancing

One trick academics use to increase their publication count is to publish very similar papers in different conferences/journals; they essentially plagiarize themselves. This practice is frowned upon, but unless referees spot the ‘duplication’, it is difficult to prevent such plagiarized versions being published. Sometimes the knock-off paper will include additional authors and may not include some of the original authors.

How do people feel about independent authors publishing a paper where all the interesting material was derived from someone else’s paper, i.e., no joint authors? I have just encountered such a case in empirical software engineering.

“Software Cost Estimation: Present and Future” by Siba N. Mohanty from 1981 (cannot find a non-paywall pdf via Google; must exist because I have a copy) has been reworked to create “Cost Estimation: A Survey of Well-known Historic Cost Estimation Techniques” by Syed Ali Abbas and Xiaofeng Liao and Aqeel Ur Rehman and Afshan Azam and M. I. Abdullah (published in 2012; pdf here); they cite Mohanty as the source of their data, some thought has obviously gone into the reworked material and I found it useful and there is a discussion on techniques created since 1981.

What makes the 2012 stand out as interesting is the depth of analysis of the 1970s models and the data, all derived from the 1981 paper. The analysis of later models is not as interesting and doe snot include any data.

The 2012 paper did ring a few alarm bells (which rang a lot more loudly after I read the 1981 paper):

• Why was such a well researched and interesting paper published in such an obscure (at least to me) journal? I have encountered such cases before and had email conversations with the author(s). The well-known journals have not always been friendly towards empirical research, so an empirical paper appearing in less than a stellar publication is not unusual.

As regular readers will know I am always on the look-out for software engineering data and am willing to look far and wide. I judge a paper by its content, not the journal it was published in

• Why, in 2012, were researchers comparing effort estimation models proposed in the 1970s? Well, I am, so why not others? It did seem odd that I could not track down papers on some of the models cited, perhaps the pdfs had disappeared since 2012??? I think I just wanted to believe others were interested in what I was interested in.

What now? Retraction watch offers some advice.

The Journal of Emerging Trends in Computing and Information Sciences has an ethics page, I will email them a link to this post and see what happens (the article in question is listed as their second most cited article last year, with 19 citations).

In the mid-70s the US Department of Defense decided it could save lots of money by getting all its contractors to write code in the same programming language. In February 1980 a language was chosen, Ada, but by the end of the decade the DoD had snatched defeat from the jaws of victory; what happened?

I think microcomputers is what happened; these created whole new market ecosystems, some of which were much larger than the ecosystems that mainframes and minicomputers sold into.

These new ecosystems sucked up nearly all the available software developer mind-share; the DoD went from being a major employer of developers to a niche player. Developers did not want a job using Ada because they thought that being type-cast as Ada programmers would overly restricted their future job opportunities; Ada was perceived as a DoD only language (actually there was so little Ada code in the DoD, that only by counting new project starts could it get any serious ranking).

Lots of people were blindsided by the rapid rise (to world domination) of microcomputers. Compilers could profitably sold (in some cases) for tends or hundreds of dollars/pounds because the markets were large enough for this to be economically viable. In the DoD ecosystems compilers had to be sold for thousands or hundreds of thousands of dollars/pounds because the markets were small. Micros were everywhere and being programmed in languages other than Ada; cheap Ada compilers arrived after today’s popular languages had taken off. There is no guarantee that cheap compilers would have made Ada a success, but they would have ensured the language was a serious contender in the popularity stakes.

By the start of the 90s Ada supporters were reduced to funding studies to produce glowing reports of the advantages of Ada compared to C/C++ and how Ada had many more compilers, tools and training than C++. Even the 1991 mandate “… where cost effective, all Department of Defense software shall be written in the programming language Ada, in the absence of special exemption by an official designated by the Secretary of Defense.” failed to have an impact and was withdrawn in 1997.

The Ada mandate was cancelled as the rise of the Internet created even bigger markets, which attracted developer mind-share towards even newer languages, further reducing the comparative size of the Ada niche.

Astute readers will notice that I have not said anything about the technical merits of Ada, compared to other languages. Like all languages, Ada has its fanbois; these are essentially much older versions of the millennial fanbois of the latest web languages (e.g., Go and Rust). There is virtually no experimental evidence that any feature of any language is best/worse than any feature in any other language (a few experiments showing weak support for stronger typing). To its credit the DoD did fund a few studies, but these used small samples (there was not yet enough Ada usage to make larger sample possible) that were suspiciously glowing in their support of Ada.

## We hereby retract the content of this paper

Yesterday I came across a paper in software engineering that had been retracted, the first time I had encountered such a paper (I had previously written about how software engineering is great discipline for an academic fraudster).

Having an example of the wording used by the IEEE to describe a retracted paper (i.e., “this paper has been found to be in violation of IEEE’s Publication Principles”), I could search for more. I get 24,400 hits listed when “software” is included in the search, but clicking through the pages there are just 71 actual results.

A search of Retraction Watch using “software engineering” returns nine hits, none of which appear related to a software paper.

I was beginning to think that no software engineering papers had been retracted, now I know of one and if I am really interested the required search terms are now known.

## Two approaches to arguing the 1969 IBM antitrust case

My search for software engineering data has made me a regular customer of second-hand book sellers; a recent acquisition is: “Big Blue: IBM’s use and abuse of power” by Richard DeLamarter, which contains lots of interesting sales and configuration data for IBM mainframes from the first half of the 1960s.

DeLamarter’s case, that IBM systematically abused its dominant market position, looked very convincing to me, but I saw references to work by Franklin Fisher (and others) that, it was claimed, contained arguments for IBM’s position. Keen to find more data and hear alternative interpretations of the data, I bought “Folded, Spindled, and Mutilated” by Fisher, McGowan and Greenwood (by far the cheaper of the several books that have written on the subject).

The title of the book, Folded, Spindled, and Mutilated, is an apt description of the arguments contained in the book (which is also almost completely devoid of data). Fisher et al obviously recognized the hopelessness of arguing IBM’s case and spend their time giving general introductions to various antitrust topics, arguing minor points or throwing up various smoke-screens.

An example of the contrasting approaches is calculation of market share. In order to calculate market share, the market has to be defined. DeLamarter uses figures from internal IBM memos (top management were obsessed with maintaining market share) and quote IBM lawyers’ advice to management on phrases to use (e.g., ‘Use the term market leadership, … avoid using phrasing such as “containment of competitive threats” and substitute instead “maintain position of leadership.”‘); Fisher et al arm wave at length and conclude that the appropriate market is the entire US electronic data processing industry (the more inclusive the market used, the lower the overall share that IBM will have; using this definition IBM’s market share drops from 93% in 1952 to 43% in 1972 and there is a full page graph showing this decline), the existence of IBM management memos is not mentioned.

Why do academics risk damaging their reputation by arguing these hopeless cases (I have seen it done in other contexts)? Part of the answer is a fat pay check, but also many academics’ consider consulting for industry akin to supping with the devil (so they get a free pass on any nonsense sprouted when “just doing it for the money”).

## Books similar to my empirical software engineering book

I am sometimes asked which other books are similar to the Empirical Software Engineering book I am working on.

In spirit, the most similar book is “Software Project Dynamics” by Abdel-Hamid and Madnick, based on Abdel-Hamid’s PhD thesis. The thesis/book sets out to create an integrated model of software development projects, using system dynamics (the model can be ‘run’ to produce outputs from inputs, assuming the necessary software is available).

Building a model of the software development process requires figuring out the behavior of all the important factors and Abdel-Hamid does a thorough job of enumerating the important factors and tracking down the available empirical work (in the 1980s). The system dynamics model, written in Dynamo appears in an appendix (I have not been able to locate any current implementation).

In the 1980s I would have agreed with Abdel-Hamid that it was possible to build a reasonably accurate model of software development projects. Thirty years later, I have tracked down a lot more empirical work and know a more about how software projects work. All this has taught me is that I don’t know enough to be able to build a model of software development projects; but I still think it is possible, one day.

There have been other attempts to build models of major aspects of software development projects (all using system dynamics), including Madachy’s PhD and later book “Software Process Dynamics”, and Buettner’s PhD (no book, yet???).

There are other books that include some combination of the words empirical, software and engineering in their title. On the whole these are collections of edited papers, whose chapters are written by researchers promoting their latest work; there is even one that aims to teach students how to do empirical work.

Dag Sjøberg has done some interesting empirical work and is currently working on an empirical book, this should be worth a look.

“R in Action” by Kabacoff is the closest to the statistical material, but at a more general level. “The R Book” by Crawley is the R book I would recommended, but it is not at all like the material I have written.

## Signed-magnitude: The integer representation of choice for IoT?

What is the best representation to use for integer values in a binary computer? I’m guessing that most people think two’s complement is the answer, because this is the representation that all the computers they know about use (the Univac 1100/2200 series uses one’s complement; I don’t know of any systems currently in use that make use of signed magnitude, pointers welcome).

The C Standard allows implementations to support two’s complement, one’s complement and signed magnitude (the Univac 1100/2200 series has a C compiler). Is it time for the C Standard to drop support for one’s complement and signed magnitude?.

Why did two’s complement ‘win’ the integer representation battle and what are the chances that hardware vendors are likely to want to use a different representation in the future?

The advantage of two’s complement over the other representations is that the same hardware circuits can be used to perform arithmetic on unsigned and signed integer values. Not a big issue these days, but a major selling point back when chip real-estate was limited.

I can think of one market where signed magnitude is the ‘best representation’, extremely low power devices, such as those that extract power from the radio waves permeating the environment, or from the vibrations people generate as they move around.

Most of the power consumed by digital devices occurs when a bit flips from zero to one, or from one to zero. An application that spends most of its time processing signals that vary around zero (i.e., can have positive and negative values) will experience many bit flips, using a two’s complement representation, when the value changes from positive to negative, or vice-versa, e.g., from 0000000000000001 to 0000000000000000 to 1111111111111111; in signed magnitude a change of sign generates one extra bit-flip, e.g., 0000000000000001 to 0000000000000000 to 1000000000000001.

Simulations show around 30% few transitions for signed magnitude compared with two’s complement, for certain kinds of problems.

Signed magnitude would appear to be the integer representation of choice for some Internet-of-Things solutions.

## Software systems are the product of cognitive capitalism

Economics obviously has a significant impact on the production of software systems; it is the second chapter of my empirical software engineering book (humans, who are the primary influencers, are the first chapter; technically the Introduction is the first chapter, but you know what I mean).

I have never been happy with the chapter title “Economics”; it does not capture the spirit of what I want to talk about. Yes, a lot of the technical details covered are to be found in economics related books and courses, but how do these technical details fit into a grand scheme?

I was recently reading the slim volume “Dead Man Working” by Cederström and Fleming and the phrase cognitive capitalism jumped out at me; here was a term that fitted the ideas I had been trying to articulate. It took a couple of days before I took the plunge and changed the chapter title. In the current draft pdf little else has changed in the ex-Economics chapter (e.g., a bit of a rewrite of the first few lines), but now there is a coherent concept to mold the material around.

## Ecosystems chapter added to “Empirical software engineering using R”

The Ecosystems chapter of my Empirical software engineering book has been added to the draft pdf (download here).

I don’t seem to be able to get away from rewriting everything, despite working on the software engineering material for many years. Fortunately the sparsity of the data keeps me in check, but I keep finding new and interesting data (not a lot, but enough to slow me down).

There is still a lot of work to be done on the ecosystems chapter, not least integrating all the data I have been promised. The basic threads are there, they just need filling out (assuming the promised data sets arrive).

I did not get any time to integrate in the developer and economics data received since those draft chapters were released; there has been some minor reorganization.

As always, if you know of any interesting software engineering data, please tell me.

I’m looking to rerun the workshop on analyzing software engineering data. If anybody has a venue in central London, that holds 30 or so people+projector, and is willing to make it available at no charge for a series of free workshops over several Saturdays, please get in touch.

Projects chapter next.

## 2017 in the programming language standards’ world

Yesterday I was at the British Standards Institution for a meeting of IST/5, the committee responsible for programming languages.

The amount of management control over those wanting to get to the meeting room, from outside the building, has increased. There is now a sensor activated sliding door between the car-park and side-walk from the rear of the building to the front, and there are now two receptions; the ground floor reception gets visitors a pass to the first floor, where a pass to the fifth floor is obtained from another reception (I was totally confused by being told to go to the first floor, which housed the canteen last time I was there, and still does, the second reception is perched just inside the automatic barriers to the canteen {these barriers are also new; the food is reasonable, but not free}).

Visitors are supposed to show proof that they are attending a meeting, such as a meeting calling notice or an agenda. I have always managed to look sufficiently important/knowledgeable/harmless to get in without showing any such documents. I was asked to show them this time, perhaps my image is slipping, but my obvious bafflement at the new setup rescued me.

Why does BSI do this? My theory is that it’s all about image, BSI is the UK’s standard setting body and as such has to be seen to follow these standards. There is probably some security standard for rules to follow to prevent people sneaking into buildings. It could be argued that the name British Standards is enough to put anybody off wanting to enter the building in the first place, but this does not sound like a good rationale for BSI to give. Instead, we have lots of sliding doors/gates, multiple receptions (I suspect this has more to do with a building management cat fight over reception costs), lifts with no buttons ‘inside’ for selecting floors, and proof of reasons to be in the building.

There are also new chairs in the open spaces. The chairs have very high backs and side-baffles that surround the head area, excellent for having secret conversations and in-tune with all the security. These open areas are an image of what people in the 1970s thought the future would look like (BSI is a traditional organization after all).

So what happened in the meeting?

Cobol standard’s work becomes even more dead. PL22.4, the US Cobol group is no more (there were insufficient people willing to pay membership fees, so the group was closed down).

People are continuing to work on Fortran (still the language of choice for supercomputer Apps), Ada (some new people have started attending meetings and support for `@` is still being fought over), C, Internationalization (all about character sets these days). Unprompted somebody pointed out that the UK C++ panel seemed to be attracting lots of people from the financial industry (I was very professional and did not relay my theory that it’s all about bored consultants wanting an outlet for their creative urges).

SC22, the ISO committee responsible for programming languages, is meeting at BSI next month, and our chairman asked if any of us planned to attend. The chair’s response, to my request to sell the meeting to us, was that his vocabulary was not up to the task; a two-day management meeting (no technical discussions permitted at this level) on programming languages is that exciting (and they are setting up a special reception so that visitors don’t have to go to the first floor to get a pass to attend a meeting on the ground floor).

## Information on computers from the 1970s and earlier

A collection of links to sources of hardware and software related information from the 1970s and earlier.

Computers and Automation, a monthly journal published between 1954 and 1978, by far and away the best source of detailed information from this period. The June issue contained an extensive computer directory and buyers guide, including a census of installed computers. The collected census for 1962-1974 must rank in the top ten of pdf files that need to be reliably converted to text.

Computer characteristics quarterly, the title says it all; the stories about the weird and wonderful computers that used to be on sale really are true. Only a couple of issues available online at the moment.

Bitsavers huge collection of scanned computer manuals. The directory listing of computer companies is a resource in its own right.

DTIC (Defense Technical Information Center). A treasure trove of work sponsored by the US military from the time of Rome and late.

Ed Thelan’s computer history: note his contains material that can be hard to find via the main page, e.g., the BRL 1961 report.

“Inventory of Automatic data processing equipment in the Federal Government”: There are all sorts of interesting documents lurking in pdfs waiting to be found by the right search query.

Books

“Software Reliability” by Thayer, Lipow and Nelson is now available online.

The Economics of Computers” by William F. Sharpe contains lots of analysis and data on computer purchase/leasing and usage/performace details from the mid-1960s.

“Data processing technology and economics” by Montgomery Phister is still only available in dead tree form (and uses up a substantial amount of tree).

Missing in Action

“A Study of Technological Innovation: The Evolution of Digital Computers”, Kenneth Knight’s PhD thesis at Carnegie Institute of Technology, published in 1963. Given Knight’s later work, this will probably be a very interesting read.

“Computer Survey”, compiled by Mr Peddar, was a quarterly list of computers installed in the UK. It relied on readers (paper) mailing in details of computers in use. There are a handful of references and that’s all I can find.

What have I missed? Suggests and links very welcome.