April 23, 2017
20 Dev Metrics - 6. Complexity (Likelihood of Failure)The sixth in my Twitter series 20 Dev Metrics has proven, over the decades, to be a reasonably good predictor of which code is most likely to have bugs - Complexity.
Code, like all machines, has a greater probability of going wrong when there are more things in it that could be wrong - more ways of being wrong. It really is as straightforward as that.
There are different ways of measuring code complexity, and they all have their merits. Size is an obvious one. Two lines of code tends to have twice as many ways of being wrong. It's not a linear relationship, though. Four lines of code isn't twice as likely as two lines of code to be buggy. It could be twice as likely again, depending on the extent to which lines of code interact with each other. To illustrate with a metaphor; four people are much more than twice as likely as two people to have a disagreement. The likelihood of failure grows exponentially with code size.
Another popular measure is cyclomatic complexity. This tells us how many unique paths through a body of code exist, and therefore how many tests it might take to cover every path.
Less popular, but still useful, is Halstead Volume, which was used back in the CMMi days as a predictor of the maintenance cost of code. It's a bit more sophisticated than measuring lines of code, calculating the size of 'vocabulary' that the code uses, so a line of code that employs more variables - and therefore more ways of being wrong - would have a greater Halstead Volume.
All of these metrics are easily calculated, and many tools are available to collect them. They make most sense at the method level, since that's the smallest unit of potential test automation. But complexity aggregated at the class level can indicate classes that do too much and need splitting up.
Posted 1 year, 2 months ago on April 23, 2017