codebase-inspection

You can count lines of code and get a language breakdown by running a pygount-based analysis on your repository. It computes file counts, LOC metrics, and code-to-comment ratios while skipping build and dependency directories for accurate repository metrics.

The best way to measure code-to-comment ratio is using pygount to analyze your codebase, which calculates the percentage of comment lines versus actual code lines. You can output the documentation density results as a summary, per-file breakdown, or JSON.

Yes, you can export repository language metrics to JSON for programmatic analysis. The codebase inspection supports flexible output formats including human-friendly summaries, detailed per-file breakdowns, and JSON outputs for tracking codebase growth over time.

You need to perform a codebase inspection to estimate refactor scope when you are planning structural changes and must quantify dominant languages. Analyzing the line-of-code counts and code-to-comment ratios helps you assess the effort required for the repository.

Your repository metrics scan may take long because it is traversing large dependency or build directories. Applying the --folders-to-skip option with pygount avoids long scans by excluding these cache and dependency folders from the line-of-code count.