Join a crowdsourced search for software engineering data

Software engineering data, that can be made publicly available, is very rare; most people don’t attempt to collect data, and when data is collected, people rarely make any attempt to hang onto the data they do collect.

Having just one person actively searching for software engineering data (i.e., me) restricts potential sources of data to be English speaking and to a subset of development ecosystems.

This post is my attempt to start a crowdsourced campaign to search for software engineering data.

Finding data is about finding the people who have the data and have the authority to make it available (no hacking into websites).

Who might have software engineering data?

In the past, I have emailed chief technology officers at companies with less than 100 employees (larger companies have lawyers who introduce serious amounts of friction into releasing company data), and this last week I have been targeting Agile coaches. For my evidence-based software engineering book I mostly emailed the authors of data driven papers.

A lot of software is developed in India, China, South America, Russia, and Europe; unless these developers are active in the English-speaking world, I don’t see them.

If you work in one of these regions, you can help locate data by finding people who might have software engineering data.

If you want to be actively involved, you can email possible sources directly, alternatively I can email them.

If you want to be actively involved in the data analysis, you can work on the analysis yourself, or we can do it together, or I am happy to do it.

In the English-speaking development ecosystems, my connection to the various embedded ecosystems is limited. The embedded ecosystems are huge, there must be software data waiting to be found. If you are active within an embedded ecosystem, you can help locate data by finding people who might have software engineering data.

The email template I use for emailing people is below. The introduction is intended to create a connection with their interests, followed by a brief summary of my interest, examples of previous analysis, and the link to my book to show the depth of my interest.

By all means cut and paste this template, or create one that you feel is likely to work better in your environment. If you have a blog or Twitter feed, then tell them about it and why you think that evidence-based software engineering is important.

Be responsible and only email people who appear to have an interest in applying data analysis to software engineering. Don’t spam entire development groups, but pick the person most likely to be in a position to give a positive response.

This is a search for gold nuggets, and the response rate will be very low; a 10% rate of reply, saying sorry not data, would be better than what I get. I don’t have enough data to be able to calculate a percentage, but a ballpark figure is that 1% of emails might result in data.

I treat the search as a background task, taking months to locate and email, say, 100-people considered worth sending a targeted email. My experience is that I come up with a search idea or encounter a blog post that suggests a line of enquiry, that may result in sending half-a-dozen emails. The following week, if I’m lucky, the same thing might happen again (perhaps with fewer emails). It’s a slow process.

If people want to keep a record of ideas tried, the evidence-based software engineering Slack channel could do with some activity.

Hello,

A personalized introduction, such as: I have been reading
your blog posts on XXX, your tweets about YYY,
your youtube video on ZZZ.

My interest is in trying to figure out the human issues
driving the software process.

Here are two detailed analysis of Agile estimation data:
https://arxiv.org/abs/1901.01621
and
https://arxiv.org/abs/2106.03679

My book Evidence-based Software Engineering discusses what is
currently known about software engineering, based on an
analysis of all the publicly available data.
pdf+code+all data freely available here:
http://knosof.co.uk/ESEUR/

and I'm always on the lookout for more software data.
This email is a fishing request for software engineering data.

I offer a free analysis of software data, provided an
anonymised version of the data can be made public.

Fitting discontinuous data from disparate sources

Sorting and searching are probably the most widely performed operations in computing; they are extensively covered in volume 3 of The Art of Computer Programming. Algorithm performance is influence by the characteristics of the processor on which it runs, and the size of the processor cache(s) has a significant impact on performance.

A study by Khuong and Morin investigated the performance of various search algorithms on 46 different processors. Khuong The two authors kindly sent me a copy of the raw data; the study webpage includes lots of plots.

The performance comparison involved 46 processors (mostly Intel x86 compatible cpus, plus a few ARM cpus) times 3 array datatypes times 81 array sizes times 28 search algorithms. First a 32/64/128-bit array of unsigned integers containing N elements was initialized with known values. The benchmark iterated 2-million times around randomly selecting one of the known values, and then searching for it using the algorithm under test. The time taken to iterate 2-million times was recorded. This was repeated for the 81 values of N, up to 63,095,734, on each of the 46 processors.

The plot below shows the results of running each algorithm benchmarked (colored lines) on an Intel Atom D2700 @ 2.13GHz, for 32-bit array elements; the kink in the lines occur roughly at the point where the size of the array exceeds the cache size (all code+data):

Benchmark runtime at various array sizes, for each algorithm using a 32-bit datatype.

What is the most effective way of analyzing the measurements to produce consistent results?

One approach is to build two regression models, one for the measurements before the cache ‘kink’ and one for the measurements after this kink. By adding in a dummy variable at the kink-point, it is possible to merge these two models into one model. The problem with this approach is that the kink-point has to be chosen in advance. The plot shows that the performance kink occurs before the array size exceeds the cache size; other variables are using up some of the cache storage.

This approach requires fitting 46*3=138 models (I think the algorithm used can be integrated into the model).

If data from lots of processors is to be fitted, or the three datatypes handled, an automatic way of picking where the first regression model should end, and where the second regression model should start is needed.

Regression discontinuity design looks like it might be applicable; treating the point where the array size exceeds the cache size as the discontinuity. Traditionally discontinuity designs assume a sharp discontinuity, which is not the case for these benchmarks (R’s rdd package worked for one algorithm, one datatype running on one processor); the more recent continuity-based approach supports a transition interval before/after the discontinuity. The R package rdrobust supports a continued-based approach, but seems to expect the discontinuity to be a change of intercept, rather than a change of slope (or rather, I could not figure out how to get it to model a just change of slope; suggestions welcome).

Another approach is to use segmented regression, i.e., one of more distinct lines. The package segmented supports fitting this kind of model, and does estimate what they call the breakpoint (the user has to provide a first estimate).

I managed to fit a segmented model that included all the algorithms for 32-bit data, running on one processor (code+data). Looking at the fitted model I am not hopeful that adding data from more than one processor would produce something that contained useful information. I suspect that there are enough irregular behaviors in the benchmark runs to throw off fitting quality.

I’m always asking for more data, and now I have more data than I know how to analyze in a way that does not require me to build 100+ models :-(

Suggestions welcome.