Code Complexity vs Churn:

Several authors have shown that the two-dimensional space generated by churn (the frequency of change of a certain file or other code entity) and complexity (e.g. cyclomatic complexity or a similar metric) conveys meaningful information about code quality.

In other words: Files that change often and contain large complexity often contain the least understood code sequences. Because that’s such an important metric and I couldn’t find a tool that covers both Ruby and JavaScript equally well, I made my own - Attractor.

Using this metric I can obtain a quick glance at the pain points in a codebase, and know where to look first to understand the application’s tech debt issues.