Using Code Metrics with Purpose
Continue Reading November 30th, 2009 Ben Northrop
I know plenty of developers who, at a tactical level, have had success with static source code analysis tools, using them to help find and root out bad code smells. When PMD tells us there’s an empty catch block at line 207, for instance, we know exactly what to do.
At an aggregate level, however, code metrics are seldom so helpful or straight-forward. When seeing that a source tree has 160,000 lines of code or an average cyclomatic complexity of 4.12, our first thought is usually “interesting!”…followed shortly by “well, now what?”.
The problem is, in my experience, we often look at our code metrics in isolation, without good comparison points, leaving us to wonder whether the numbers we see are big or small, typical or abnormal, good or bad. In the end, it’s not clear what to do, if anything.