I met Nigel at the Erlang meeting I attended a few months ago and he didn't create a particularly good impression. Lots of talk, but mostly other people's ideas rather than any insights of his own. Obviously, the following is coloured by my initial impression, so please take that into consideration.
Edmund: Well, there’s Grand Duchess Sophia of Turin. We’ll never get her to marry him. Baldrick: Why not? Edmund: Because she’s met him.
A while ago, I got a book on software metrics called "Codemetrics":
The author, Jonathan Alexander, had read about the use of Sabermetrics (analysing baseball statistics) in creating baseball teams and has applied a similar method to software teams. It's an interesting theory and the book illustrates his point well.
I thought that this might be along the same lines, or maybe about software complexity measures, but it was mostly about the falability of software estimation, something that everyone knows about but can do little to remedy. Unfortunately, Nigel had no new suggestions. He quoted a lot of smart people, including Tom DeMarco whose excellent book "Peopleware" is now available in digital format, Victorian scientist Lord Kelvin and even Fred Brooks got a look-in.
Overall, the presentation was long-winded and meandering: not once was an actual software metric explained in any detail, nor mentioned to my recollection, although I didn't stay to the end. Worse, he quoted some statistics about software failures and reached a conclusion that was highly suspect: software projects above $10 million have a high failure rate, so the more expensive a project, the more likely it is to fail. This illustrates a maxim in statistics called "correlation is not causation", meaning that because two sets of statistics seem to vary in relation to each other doesn't mean they actually are connected. Failed projects cost a lot of money, but a projects' cost has nothing to do with it's likelyhood of failure.
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