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Monday, August 20, 2012

The Science of Development

Despite the study of computing being termed "Computer Science", most working software developers will agree that development is more art than science. In most application code, the hard decisions are not which algorithm to use or which data structure makes the most sense. Rather, they tend to focus on how to build out a big system in a way that will let lots of people contribute to it, how to build a system such that when you go back to it you can understand what's going on, how to make things that will last longer than 6 months. While occasionally those decisions have a "right" answer, more often they are the type of judgement calls that fall into the art of computer programming.

There are, however, two very important areas in which science, and by science I mean careful observation and logical deduction, play a very important role. These two areas are debugging and performance analysis. And where developers are often good at the art of programming, many of us fall short on the science.

I've been meaning to write a blog post about what makes a person a truly great debugger. This will not be that post, but in thinking about the topic I polled some of the people I believe are great debuggers including my longtime mentor. His response was interesting and boils down the concepts very succinctly. Good debuggers have a scientific bent. Some people, when faced with a bug, will attempt to fix it via trial and error. If changing the order of these statements makes the threading problem go away, the bug must be solved by that change. Good debuggers, on the other hand, know that there is a root cause for errors that can be tracked down, and they are able to break down the problem in a methodical way, checking assumptions and observing behavior through repeated experiments and analysis.

It's a wise point, and one that I must agree with. If I observe myself debugging hard problems, a few things come out. One, I always believe that with enough patience and help I can in fact find the root of the problem, and I pretty much always do. Two, when I dig into a tough problem, I start keeping notes that resemble a slightly disorganized lab notebook. In particular when debugging concurrency problems between distributed systems, to get anywhere sensible you have to observe the behavior of the systems in correct state, and in problem state, and use the side-effects of those correct and incorrect states (usually log files) to build hypotheses about the root cause of the problem.

I've also been thinking lately about this scientific requirement in performance analysis and tuning. We're going through a major performance improvement exercise right now, and one of the things that has caused us challenges is a lack of reproducible results. For example, one of the first changes we made was to improve our CSS by converting it to SCSS. We were relying on New Relic, our site metrics monitor, to give us an idea of how well we improved our performance. This caused us a few problems. One, our staging environment is so random due to network and load that performance changes rarely show up there. Two, New Relic is not a great tool for evaluating certain kinds of client-side changes. Lacking any sort of reliable evaluation of performance changes in dev/staging caused us to make a guess, SCSS would be better, that took a week plus to pan out, and resulted in inconclusive measurements. We were looking for luck, but we need science, and our lack of scientific approach hampered our progress. Now we have taken a step back and put reproducible measures in place, such as timing a set of well-known smoke tests that run very regularly, and using GTMetrix to get a true sense of client-side timings. Our second performance change, minified javascript, has solid numbers behind it and we finally feel certain we're moving in the right direction.

Is there art in debugging and performance tuning? Well, there is the art of instinct that lets you cut quickly through the noise to the heart of a problem. Well-directed trial and error (and well-placed print statements) can do a lot for both debugging and performance analysis. But getting these instincts takes time, and good instincts start from science and cold, hard, repeatable, observable truths.

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