You Want IT When?

When I was a child, I had a strong aversion to pineapples.  Just the look of them made me ill.  It didn’t matter whether it was cut or uncut; there was just something about the look that made me believe I would not like the taste.  Maybe it was the color yellow, but there was something terribly unappealing about the fruit, and no matter how much my mom would entice me with declarations of how sweet it taste, I would not try it.

I felt the same way about cranberry sauce too.  It was an emotional aversion; there was nothing logical about it even though I was convinced my reasons were all logical.  Cranberry sauce looks slimy; slimy is disgusting; therefore, it must taste like it looks: disgusting.  It’s a logical inference; though, it has little relevance to how cranberry sauce actually tastes.

As I got older, I became more open to giving foods a try (and other things, of course) that were unappealing to me.  When I finally gave pineapples and cranberry sauce a try, I discovered how I’d been missing out for so long on enjoying a food that was so pleasurable to me.

Much of the software community has a similar aversion to LOC. Many of their arguments against LOC are logical, but they aren’t relevant to the science and practice of LOC as advocated by its adherents.  Sure one can write a line of code with more defects than ten lines of code, but the Law of Big Numbers says the density observed in practice will be the expected value.

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During a presentation on software metrics, I asked the audience to estimate the distance from New York, NY to Sante Fe, New Mexico in Kilometers (they were asked not to participate if they already knew the answer), and I asked them not to do any conversions in their head before giving their answer.  For those of you reading in other countries, we use miles to measure long distances in the USA, so kilometers is a unit that we don’t have much experience using.   The answers to the question were all over the place with most of them being overestimates of signification magnitude, double or more.  Then I asked the audience to estimate the distance again, but this time, they were to estimate the distance in miles - a unit they were all very familiar with.  These estimates were much better with all being less than 20% away from the actual distance.   Some were so close to the actual distance that they were essentially accurate.

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I often wonder why software teams always seem to be committing to unrealistic schedules.  You know when the sales team signed a contract with a customer to deliver functionality on a date without ever asking the engineering team whether it were possible. Never mind the roadmaps identify an entirely different set of functionality than what was committed. And guess what?  The product roadmaps can’t change either; the sales team has signed contracts on that functionality too.

It’s not just the sales organizations though; the software organizations are all too happy to over commit without any help from outsiders.  You probably have one of those programmers on your team that when you ask him for an estimate, he says a week and three months later he’s still working on it.  When you ask him what happened, his answer is well it was a bit more complicated than I thought.  Does he learn from it? No way. Ask for an estimate on the next project, and he’ll still quote a week.

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