Optimal sizing of a product backlog

Developers working on the implementation of a software system will have a list of work that needs to be done, a to-do list, known as the product backlog in Agile.

The Agile development process differs from the Waterfall process in that the list of work items is intentionally incomplete when coding starts (discovery of new work items is an integral part of the Agile process). In a Waterfall process, it is intended that all work items are known before coding starts (as work progresses, new items are invariably discovered).

Complaints are sometimes expressed about the size of a team’s backlog, measured in number of items waiting to be implemented. Are these complaints just grumblings about the amount of work outstanding, or is there an economic cost that increases with the size of the backlog?

If the number of items in the backlog is too low, developers may be left twiddling their expensive thumbs because they have run out of work items to implement.

A parallel is sometimes drawn between items waiting to be implemented in a product backlog and hardware items in a manufacturer’s store waiting to be checked-out for the production line. Hardware occupies space on a shelf, a cost in that the manufacturer has to pay for the building to hold it; another cost is the interest on the money spent to purchase the items sitting in the store.

For over 100 years, people have been analyzing the problem of the optimum number of stock items to order, and at what stock level to place an order. The economic order quantity gives the optimum number of items to reorder, Q (the derivation assumes that the average quantity in stock is Q/2), it is given by:

Q=sqrt{{2DK}/h}, where D is the quantity consumed per year, K is the fixed cost per order (e.g., cost of ordering, shipping and handling; not the actual cost of the goods), h is the annual holding cost per item.

What is the likely range of these values for software?

  • D is around 1,000 per year for a team of ten’ish people working on multiple (related) projects; based on one dataset,
  • K is the cost associated with the time taken to gather the requirements, i.e., the items to add to the backlog. If we assume that the time taken to gather an item is less than the time taken to implement it (the estimated time taken to implement varies from hours to days), then the average should be less than an hour or two,
  • h: While the cost of a post-it note on a board, or an entry in an online issue tracking system, is effectively zero, there is the time cost of deciding which backlog items should be implemented next, or added to the next Sprint.

    If the backlog starts with n items, and it takes t seconds to decide whether a given item should be implemented next, and f is the fraction of items scanned before one is selected: the average decision time per item is: avDecideTime={f*n*(f*n+1)/2}*t seconds. For example, if n=50, pulling some numbers out of the air, f=0.5, and t=10, then avDecideTime=325, or 5.4 minutes.

    The Scrum approach of selecting a subset of backlog items to completely implement in a Sprint has a much lower overhead than the one-at-a-time approach.

If we assume that K/h==1, then Q=sqrt{2*1000}=44.7.

An ‘order’ for 45 work items might make sense when dealing with clients who have formal processes in place and are not able to be as proactive as an Agile developer might like, e.g., meetings have to be scheduled in advance, with minutes circulated for agreement.

In a more informal environment, with close client contacts, work items are more likely to trickle in or appear in small batches. The SiP dataset came from such an environment. The plot below shows the number of tasks in the backlog of the SiP dataset, for each day (blue/green) and seven-day rolling average (red) (code+data):

Tasks waiting to be implemented, per day, over duration of SiP projects.

How fresh is your backlog?

Do you struggle with an overwhelming backlog?

Do you count the number of product backlog items in your backlog in tens? hundred? or thousands?

Does your backlog contain many stories which have been there for months, if not years, and yet never raise to the top of the backlog?

Is your success judged on your ability to do the backlog?

Backlogs were a good idea when they were introduced a bit over 20 years ago but today many teams slaves to the backlog – see my posts on the Tyranny of the backlog and Purpose over backlog. One of the benefits I’ve called out for OKRs is the ability to move away from backlog driven development (BLDD).

In Succeeding with OKRs in Agile I ask suggest you either need to prioritise your backlog over OKRs (in which case OKRs are derived from the backlog you intend to do) or OKRs over backlog (in which case OKRs are derived from strategy and the backlog plays a supporting role.) In my podcast with Jenny Herald earlier this year I even say “Let OKRs drive… nuke the backlog.”

Filipe Albero Pomar recently shared his backlog freshness blog which I think is great. Freshness is a great way to think about the state of the backlog that separates the size of the backlog from the relevance of the backlog.

Filipe’s idea is simply to talk about the backlog in terms of freshness – you have a fresh backlog if your backlog items are fresh: written recently, relate to current opportunities, problems and things people currently want.

And of course, the opposite of fresh: stale, stories that have been sitting in the backlog for months, even years, stories that relate to yesterday’s problems and project, stories which people wanted last year. The existence a big backlog of stale stories means the team is seen to be not delivering, the end-date is far off because people still expect all the work to be done.

Filipe suggests backlog freshness can be measured:

1. Set a cut-off date

2. Categorise stories as fresh or stale: fresh stories have been written since the cut-off date, those which are older are stale

3. Calculate freshness as a percentage of fresh from the total: if 25 stories out of 60 have been written in the last month then the backlog is 41% fresh, and 59% stale

Thats a useful metric, I think we can do better, look at the graphic above. I group backlog items into age groups and graphed them. For completeness I added a line to indicate average story age. Clearly this backlog is not fresh – nearly half the stories are over a year old.

I like the idea of graphing backlog freshness because it is easy to understand and makes an impact. In the graph above I’ve categorised backlog items into age groups and added an average line. Clearly this is not a fresh backlog. Whether this is the way to demonstrate backlog freshness I’m not sure – I’m playing with a histogram and quartile ranges.

With some clients I’ve thought of the backlog like a mortgage. There is the principle (the existing backlog), the interest rate (the growth rate of the backlog) and the monthly repayments (stories reaching done). Unfortunately when you do this you sometimes find the mortgage will not be paid for many years, and perhaps never. (Don’t worry about estimating the size of stories, for this sort of analysis the number of stories will get you started, and if your backlog is measured in hundreds of items the small will offset the large.)

I’d love to talk more about this and experiment with some ideas, I think it could be a very useful way of thinking about the backlog.


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Purpose over Backlog

Backlogs are a good idea. Backlogs ease the transition from the old “requirements up front” world to the new more dynamic agile world. Backlogs provide a compatibility layer for agile teams to interface to more traditional project management and governance. Backlogs even allow you to take a stab at done date!

Backlogs allow you to even out work between the quiet periods and the busy times. Backlogs give you a place to store good ideas which you can’t do just now. And because stakeholders can see their request is not forgotten they don’t need to shout for it today.

Yes backlogs are good. I’ve seen them work well myself and I’ve taught many teams to effectively use backlogs.

But – you knew there was a but coming didn’t you? – but…

Backlogs have problems, too many teams are labouring under the Tyranny of the Backlog, they have become backlog-slaves and practice something we might call BLDD – Back Log Driven Development.

(To be clear, when I say “backlog” I am primarily thinking of the product backlog – the long list of all the things the team (might) do in the future. This is different to the sprint backlog (iteration backlog). The sprint backlog is a shorter list of things the team aims to do this iteration. I am using Scrum terminology but the ideas are pretty much “generic agile” and I’m thinking more broadly than Scrum. Many implementations of Kanban feature a product backlog of sorts so while Kanban is less prone to these problem it is not immune.)

1) Lump of Work Fallacy

There is usually an assumption that the backlog represents all the work to be done – an impression reinforced by early implementations of Scrum. In the short term that leads to agile teams being seen as inflexible and prioritising process over need because new work is not allowed in.

In some cases teams even struggle to get started on work because a big-up-front requirements gathering and analysts activity is required to create a backlog. In the worst cases that work is even estimated and end-dates forecast before a line of code is cut or developers hired.

In the longer term it is simply unrealistic to assume the backlog is fixed. Even with more and better analysis it is impossible to foreseen future requests. The agile adage “it is in doing the work that we understand the work” cuts both ways: coders understand what they need to build and customers/stakeholders/analysts understand what they want.

Work will arrive after you begin, any system that does not incorporate that truth will fail one-way or another.

2) Bigger then you think

Not only does the backlog grow with completely new work the work in it changes – and grows. There are many reasons this happens: new opportunities appear, hidden ones become clear, requests require more work than expected and so on.

Humans are very bad at estimating, especially about the future, and, it turns out, they are also very bad at estimating time spent in the past. If you want accurate forecasts you need to invest in them, you need to make structural changes and you need to use statistics.

However, because of the lump of work fallacy and the belief that humans can make estimates, poor end-date projections get made and when they are missed (because they were wrong to start with) everyone gets upset.

3) Fallacy of Done

Backlogs come with burn-down charts and an assumption that there is an end; and that end is when everything is “done.” The team will be done when the backlog is empty. That assumption is baked into BLDD, traditional project management and even governance.

I have long argued that software is never done. I’ll accept that I might be wrong, but in the digital age, when business runs on digital technology (i.e. software) your products are only done when you business is done. The technology is the business, and the business is the technology. Stop the backlog growing, stop growing you technology and you kill the business.

4) Backlog Bottomless pit

Put all those reasons together and the backlog becomes a bottomless pit. In the early days of agile, when I managed teams myself, the backlog would often sit on my desk, written out on index cards and held together with rubber bands. I could get a sense of how big the backlog was my looking.

Today everyone uses electronic tracking systems. Not only do these allow an infinite number of items they rob us of perspective. To paraphrase Comrade Stalin: “2 outstanding backlog items is tragedy, 200 is a statistic.”

5) Backlogs obscure strategy & purpose

With so many backlog items it is easy to get lost – you can’t see the wood for the trees. Arguments over what will be done next start to resemble deciding who should get a lifeboat place on a sinking ship, add in the demands “when will you be done?” (plus explaining why the date has changed) and “the bigger picture” gets lost.

In Back Log Driven Development the sense of purpose and strategic goals is lost as teams struggle with the day-to-day demands of just doing stuff.

6) Powerless product owner (i.e. backlog administrators)

Tyranny of the backlog seems worst were product owners lack real authority and skills. They are little more than backlog administrators. They spend most of the week adding requests to the backlog, then passing a few chosen items to developers in planning meetings. A vicious circle develops, the product owner can’t win so people trust them less, their authority wanes, and the backlog spirals.

Few organisations give product owners the power needed to get a grip on this situation. Indeed, many product owners are plucked from the ranks for development or support and given a battlefield promotion to product owner but lack the skills required. (See The problem with Product Owners.)

A solution?

For years I’ve been suggesting teams throw away the backlog – you will not forget the important things. But then how do you know what to do?

Take a step back, start with your purpose, your mission, the reason you team, your company, your organisation exists. What should you be doing? How can you fulfil that purpose and sever your customers?

This is where I see a role for OKRs and jobs to be done. Both these techniques – together, or separately – can be used as story generators. Every time you need to more work, more stories, you return to your OKRs and ask “what can we do now to move us towards our objective?”

When writing Succeeding with OKRs in Agile I became more and more convinced this is the path to take. Increasingly I sum this up as Purpose over Backlog.

Step 1: Clarify your purpose – what is your overarching reason for existing?
Step 2: Clarify how your existing strategy builds towards that purpose, and if you don’t have a strategy create one.

Repeat steps 1 & 2 annually.

Step 3: Think broadly, set your OKRs as a team so you build towards your purpose by following your strategy.
Step 4: Spend the next 12 weeks executing against those OKRs

Repeat steps 3 & 4 every 3 months.

Step 5: In each planning meeting take stock of what you have done and progress against OKRs
Step 6: Ask “what do we need to do next to move towards the OKRs?”

Succeeding with OKRs in Agile

Repeat steps 5 and 6 every 2 weeks

And if you are Kanban’ing then keep steps 1, 2, 3 and 4, adjust 5 and 6 as appropriate.

Having finished, completed, published Succeeding with OKRs I really wish I had been clearer in the book. The ideas are there but with time they have become so much clearer… maybe I need another book.

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