Software estimation. Ah, a never-ending story. Chances are good that, whenever you’re talking about building software, this subject will pop up soon. You can be pretty sure that basically everyone around has problems with estimation or simply struggles with it. And that’s virtually obvious that there would be a new sexy method of estimation every year or so. The method which is claimed to solve an unsolvable puzzle.
Recently there was yet another question on estimation on Project Management StackExchange. This sole fact isn’t probably worth writing a whole blog post about that, but there was one side thread which is worth focusing at.
One of advices I shared was that whenever you can you should avoid estimation at all. This sprung sort of objection. OK, so the subject definitely is worth wider discussion.
First things first. Why do we estimate at all in the first place? Well, we usually want to know how much time it’s going to take to build this damn thing, don’t we? Um, have I just said “usually?” Meaning, “not always?” Actually yes. It’s not that rare when we either don’t need any estimate at all or we just want to have a general insight whether the project will be built in hours, days, weeks, months or years. In either of these cases just a coarse-grained wild-ass guess should be fine. If it’s needed at all.
OK, but what about majority of cases when we need some kind of real estimate? For example all those fixed price projects where estimates are basically a part of risk management, as the better the estimate is the smaller are chances that the project goes under water. I can’t deny that we need to have something better than wild-ass guess then.
Yet, we still can avoid estimating quite often.
Let me start with one of obvious things about estimation: if you base on historical data, and you apply them in a reasonable way of course, you can significantly improve your estimates. In other words, no matter the method, if you are just guessing how much something is going to take, you will likely to end up with way worse results when compared to a method, which uses your track record. And yes, I just dared to name planning poker “guessing.” It is collective, involves discussion, etc but usually it is just this: guessing.
Cool, let’s use historical data then. What’s next? My next question would be: how precise must your estimates be? Seriously, what kind of precision you aim for? My point is that we need very precise estimates very rarely. This is by the way the reason why I don’t use Evidence Based Scheduling anymore.
Anyway, ask yourself a question: how much you would pay for bringing your estimates to the next level of precision. Think of it like being correct in terms of estimating in years, months, weeks, days, hours, etc. Let’s take just an average several-month-long, fixed-priced type of project.
If I’m wrong with years I’m totally screwed, thus I’d pay significant part of project budget to be correct on such level. If I’m wrong with months it might be a hit on our reputation and also it may consume our whole profit we get of the project, so I’d be ready to invest something around the profit to be correct with months. Now weeks. Well, a week here, a week there, does it make such a difference in this kind of project? Can’t I just assume there is some variability here? Unless of course our deadlines are written in stone, e.g. you adjust your software to law changes. In most cases I’d invest just a handful of bucks here at best. Days? Hours? Are you kidding? Does it even make a difference that I spend a day more on such project?
Now you know what kind of precision you expect from your estimates. Would it be possible for you to come up with estimates of such precision basing purely on historical data? I mean, can’t you just come up with a simple algorithm which automatically produces results reasonable enough that you can forget about all the effort spent on estimation?
Whenever we come to discussing estimation I like to share the story from one of my teams: basing on a fact that we were collecting data on our cycle times and we reduced variability in task sizes coming up with the idea of standard-sized features we were able to do a very good job with estimates not estimating at all. We were simply breaking work down so we could learn how many features there are to build and then we were using very simple metrics basing on our track record to come up with the numbers our sales department requested. By the way: a funny thing is, almost all of that appeared as an emergent behavior – something we started doing as a part of continuous improvement initiative.
Either way, even though we were capable of providing reasonably precise and reliable estimates we didn’t really estimate. I was surprised how easy it was to get rid of estimation, but then I can come back to the point from the beginning of the article: what is the point of estimation? You don’t do it just for the sake of performing the task; you do it for a reason. As long as you achieve your goal, does the method really matter? It means that you can get rid of estimating even if you do need some kind of estimates.