The Shape of Code

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):

where: is the number of people on a team, is the time in minutes the team spent estimating and is time in minutes the team spent planning the task implementation.

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?

Update: I forgot to mention that including the date of the workshop in the above model increases the variance explained to 90%. The date here is a proxy for the task being solved. A model that uses just the date explains 75% of the variance.