Code Evolution

Code Behavior Analysis

Understand how your codebase evolves over time by mining insights from your Git history.

This application implements 10 behavioral analysis techniques, that can help to reveal a different dimension of your software's evolution.

Together, these analyses can also serve as architectural fitness functions, helping you continuously assess and guide your system's design over time.

These metrics are intended as diagnostic tools. When a specific number becomes the focus, people understandably try to improve the number itself, even if that does not always improve the underlying situation. Don't forget the Goodhart's Law:

"When a measure becomes a target, it ceases to be a good measure."

Charles Goodhart

Use these analyses to start conversations, not to assign blame.

More information on these techniques can be found in Code Behavior Analysis

1
Change Frequency / Hotspot Map

Discover the most frequently changed files in your codebase. High-change files signal risk areas and technical debt.

2
Temporal Coupling

Identify files that often change together. Hidden dependencies indicate architectural coupling beyond what the code structure shows.

3
Change Over Time

See the evolution of activity across your codebase. Understand trends, seasonal patterns, and development velocity.

4
Bus Factor

Understand who knows which parts of the code. Identify knowledge silos and single points of failure in your team.

5
Commit Size Distribution

Measure change granularity and commit patterns. Large commits are harder to review and more likely to introduce bugs.

6
Refactoring vs. Feature

Track the balance between maintenance and features. A healthy codebase invests in both new functionality and structural improvements.

7
Developer Collaboration

Visualize team collaboration patterns. See how developers interact through shared file changes and knowledge transfer.

8
Lead Time

Track delivery pace and workflow efficiency. Understand how quickly changes flow from commit to production.

9
Cyclomatic Complexity

Measure code complexity through branching paths. High complexity means harder testing, more bugs, and costlier changes.

10
Behavior + Complexity

Combine change frequency with cyclomatic complexity to find the true high-risk zones — files that are both complex and constantly changing.