The Road to Performance Is Littered with Dirty Code Bombs
There are two distinct sources of performance problems: those from configuration and those from code. Getting rid of performance problems you've coded into your system will necessarily involve refactoring. When you set out to performance tune, every bit of the system that is overly complex or highly coupled is a dirty code bomb laying in wait to derail the exercise. While traveling down a smooth road makes it easy to predict when you'll reach your destination, lining that same road with dirty code makes it just about impossible to make a sane prediction.
Consider the case where you find an execution hot spot. You will quickly realize that you're going to need to reduce the strength of the underlying algorithm by recoding it. Let's say you respond to your manager's request for an estimate with an answer of 3-4 hours. You apply the fix only to realize that you’ve broken a dependent part. Since closely related things are often necessarily coupled, you expected this to happen. But what happens if that dependent part is far away and what happens if in fixing that dependency you break other dependent parts. The farther away from the dependency is from the source the less likely you are to recognize it and account for it in your estimate. Breaking even more dependent parts will have just put your schedule at risk if it already hasn’t been blown. All of a sudden your 3-4 hour estimate may have ballooned to 3-4 weeks, often 1 or 2 days at a time. In cases where the code was highly coupled, "quick" refactorings ended up taking several months to complete. In these cases, damage to the credibility and political capital of the refactoring team has ranged from severe to terminal. If only we had a tool to help us identify and measure this risk.
In fact we have many ways of measuring the degree and depth of couplings and complexity of our code. Included are the Law of Demeter, Coupling between Objects (CBO), fan in, fan out, efferent and afferent couplings, McCabe's cyclometric complexity, and so on. These metrics describe features in our code that we can look for and count. Moreover, the magnitudes of these counts infer code quality. Consider fan out for example. Fan out is defined as the number of classes that are dependent upon a class of interest. Think of it as the number of classes that must be compiled in order to compile the class of interest. If for every class in an application this value is small, you can conclude that couplings were shallow. Conclusion: couplings pose a minimal risk were I to refactor.
A downside of software metrics is that the huge array of numbers that metrics tools will produce can be intimidating to the uninitiated. That said, software metrics is a powerful tool in our fight for clean code that can help us eliminate dirty code bombs before they are a serious risk to a performance tuning exercise.
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