The Smaller The Better

A while ago, I wrote a couple of posts about agile estimation and specifically, how I evolve estimates as projects run. I also wrote about the #NoEstimate movement what I take away from that view of estimation.

There is also a little programming exercise I was shown once, known as 'the Elephant Carpaccio' by Alistair Cockburn. One of the agile signatories I have a lot of time for. The aim of the contemporary version is to find out how thinly you can slice an elephant (aka your code) so you can write a test, code and then deploy a tiny tiny change, perhaps even one single line, into production.

What is interesting is that the smaller the change, the less the variance on that change. I sometimes do this by asking a group of folk to estimate the size of a line, which is usually quite small (in the order of an inch or so) and then ask folk to estimate the size of a much larger [elephant sized] line. What you almost always see is the standard deviation of estimates of large line size are almost always much higher than those in the smaller ones.

You see this in both story point estimates and indeed the variance of waterfall/RUP/no-method projects as a whole. No doubt we've all been in companies where small projects haven't really been that late, or cost that much more than predicted, whilst larger, more complex projects have taken or cost several orders of magnitude more (luckily, not that many at all for me).

Now, you should know by now that I like to prove things. Using empirical data and statistics to find out perhaps useful angles on things or validate a hypothesis. This case is no exception. I am going to use a previous dataset for this, gleaned form issue tracking tickets, their estimates and the cycle time of each ticket (duration the ticket spent from being opened to being closed at done).

Basically, I am testing the hypothesis that the standard deviation of higher valued tickets is greater than lower valued ones.

Method

Taking the number of tickets, their point sizes and their durations, I constructed a table of averages versus standard deviations for all Fibbonacci sized ticket (1,2,3,5,8).

Results

The results of the analysis are shown below:

The key thing to note is that the standard deviation for a 1 point story (+ or - 1.088) is significantly smaller than any of the others, with intuitively, an 8 point story having a larger deviation.

Why Care?

This is important because if you want a level of predictability, not just with time, but with effort, cost and anything else, the indication is to make the stories as small as possible.

The key thing with agile processes, is they fall into a class of statistical process. Specifically, an Ito process, akin to Brownian motion (I know, this is where it gets sexy).

Each individual ticket can be considered to have a 'predictable' component and a random variation around that. Stochastics is more than pure probabilistic methods, either in classical statistics, where predictability is not assumed to exist at all, or Bayesian statistics (where the posterior probability is gained by improving the a priori statistic, given the presence of new empirical evidence). For devs this is like knowledge improving as you gain more knowledge of the domain, which manifests when the team 'learn' through experience (I personally think Bayesian statistics holds the greatest promise of modelling an agile development process by far. Another story for another day). Stochastics assumes there is a level of determinism and a random component which us agile practitioners could consider to be caused by sickness, additions of new folk, unforeseen circumstances, team members leaving, meddling or whatever else can affect the flow of the team.

The result of the above experiment, as well as carpaccio exercises and my 'estimate the line' game all seem to suggest that if we make the tasks as small as possible, simultaneously reduce the variance. Eventually you'll find the variance from the expected time is so small as to be negligible. So keep things small and keep your chances of success high!