Objective: Analyze the codebase's commit history to identify areas with high churn rates (frequent changes), which often indicate areas that are complex, error-prone, or in need of refactoring.
Instructions:
- Access the Version Control System (VCS): Obtain the commit history from the codebase's version control system (e.g., Git).
- Calculate code churn: For each file or code module, calculate the churn rate, which can be defined as:
- Number of times the file has been modified
- Number of lines of code added, deleted, or changed over a specific period.
- Identify hotspots: Identify files or modules with significantly higher churn rates compared to the rest of the codebase.
- Investigate the reasons for high churn: Try to understand why these hotspots are frequently modified. This might involve:
- Looking at commit messages associated with the changes.
- Analyzing the types of changes made (bug fixes, new features, refactoring).
- Correlate with other metrics: Combine churn analysis with other code quality metrics (e.g., complexity) to get a more comprehensive view of problematic areas.
Expected Output: A report or visualization (e.g., a heatmap) that highlights:
- Files or modules with the highest code churn rates.
- Trends in churn over time (e.g., increasing or decreasing churn).
- Potential reasons for high churn based on commit history analysis.
- Recommendations for refactoring or further investigation.