Reading beside the lines: Using indentation to rank revisions by complexity

Maintainers often face the daunting task of wading through a collection of both new and old revisions, trying to ferret out those that warrant detailed inspection. Perhaps the most obvious way to rank revisions is by size in terms of lines of code (LOC); this technique has the advantage of being both simple and fast. However, it is well known that the vast majority of revisions are quite small, and so we would like a way of distinguishing between simple and complex changes of the same size. Classical complexity metrics, such as Halstead’s and McCabe’s, could be used but they are hard to apply to code fragments written in multiple programming languages. We propose using the statistical moments of indentation as a lightweight, language independent, revision/diff friendly metric as a proxy for classical complexity metrics. We have evaluated our approach against the entire CVS histories of the 278 of the most popular and most active SourceForge projects. We found that our results are linearly correlated and rank-correlated with traditional measures of complexity, suggesting that measuring indentation is a cheap and accurate proxy for code complexity of revisions. Thus ranking revisions by the standard deviation and summation of indentation yields results that are very similar to ranking revisions by complexity.