Stephen A. Fuqua (saf)

a Bahá'í, software engineer, and nature lover in Austin, Texas, USA

Points on Bugs and Spikes

Should bugs and spikes receive story points to aid in sprint capacity planning? Some teams will estimate all work items by time during sprint planning in order to find the right commitment. Many teams hate this and/or spend an inordinate amount of time arguing about time. Those that abandon time may be tempted to put points on these unplanned, non-productive items, but there is a cost: the completed velocity will overstate the projected release timeline for the remainder of the release backlog.

Possible solution: track the ratio of story to non-story points and use that to pad out the release projection estimate.

Story point estimation has proven to be an effective tool for providing a general sense of time/complexity of effort on a discrete tasks. Over the course of a several sprint iterations, a team with a thoughtful product backlog and consistent work environment should begin to understand roughly how many points can be completed in upcoming sprints: the velocity. With sufficient statistics, a ScrumMaster can project out not only the average velocity per sprint, but also a confidence interval.

A team engaged in releasing software every few months might reasonably estimate the entire known backlog for the next release. But what about technical spikes and bug fixes? Spikes are timeboxed explorations that ask questions, the answers to which inform future story estimates and solutions. Bugs of course are corrections to previously-built behavior.

Many people choose not to put points on spikes and bugs because they are not stories — they are not directly providing productive value to the end-users. Others do put points on spikes and bugs for the purpose of sprint capacity planning. The two goals of planning a sprint’s activities and projecting a range of potential release dates are at odds. To illustrate the dilemma, let’s consider a team with the following data:

Sprint Points Completed
One 22
Two 19
Three 23
Four 25
Five 23
Six 24
Statistics μ = 23, σ = 2

The backlog for the next release has been estimated at 83 points. How many sprints is that?

  • 83.0/(23-2) = 3.95
  • 83.0/(23+2) = 3.32

Thus the team estimates that it needs four sprints to complete the release based on the current scope of the release backlog.

The team has received two bug reports from the user community and wishes to work on them in the next sprint, and they have identified a one day spike that is expected to resolve lingering doubt about one of the story estimates. The team prefers to push themselves rather than rest on a baseline, so during planning they decide to aim for twenty-four points worth of work. But how should they account for the bugs and the spike?

The team decides to put points on them; one of the bugs is simple looking and gets one point; the other and the spike are assigned two points each. Then the team picks out nineteen points of story work to round out the sprint commitment.

And now the release backlog has 88 points instead of 83, resulting in a range of:

  • 88.0/21.0 = 4.1905
  • 88.0/25.0 = 3.52

Probably done in four sprints, but possibly in five with the worst-case projection.

In solving their sprint capacity-planning, did they discover that the roadmap was potentially off by an entire sprint due to the two bugs and the design uncertainty? If so, it was good to discover that now. And would have been nice to predict even sooner.

On the other hand, what if these aren’t their first bugs or spikes? What if twenty percent of their completed points over the past six sprints were for bugs and spikes? Then the release projection — which only contained known upcoming user stories — was based on an inflated velocity. In that case, the five points added during this sprint will likely re-occur.

For the remainder of the time until release, scrutiny of the software may continue finding small bugs and discover usability rework, while the number of spikes will go down. Lacking any data to say otherwise, it might be reasonable to assume then that there will continue to be four or five points of non-story work in the remaining sprints as well.

Thus the 83 estimated story points account for only eighty percent of the remaining effort. And this can only be seen by separating the story points from the non-story points. Now the release projection looks more like:

  • (83.0*1.2)/21.0 = 4.7429
  • (83.0*1.2)/25.0 = 3.984

That is, it would be more accurate to estimate that the release will be ready in four or five sprints. And if the product owner really wants to hit four sprints, then they’ll need to cut around 25 points from the release to be on the safe side.

What about taking the opposite tack? That is, subtract the bug and spike points from the point total before calculating velocity. That is certainly a viable approach and perhaps worth experimenting on. What I like better about the first approach:

  1. If there are going to be spikes, bugs, and rework, then it is nice to have the extra data to apply to the future projections proactively. Sure, a reduced velocity would have also padded out the sprint length. With the first approach it would be easier to justify lowering the padding if their is less non-story work than “arbitrarily” raising the expected velocity.
  2. Better respects the teams’ desire to track their progress and throughput.

Posted with : Tech, Software Development Life Cycle