codebase-inspection

To count lines of code and get a language breakdown, analyze the codebase with pygount to generate summary metrics and per-language breakdown. This delivers LOC counts and language distribution for repositories of any size.

A code-vs-comment ratio measures the proportion of comments relative to executable code lines. Analyzing this ratio helps assess documentation clarity and codebase health, indicating whether a project has sufficient explanatory comments for maintainability.

Yes, you can exclude noisy directories when analyzing a repository's language composition. The codebase analysis executes pygount while excluding specified noisy directories to ensure accurate repo stats and reliable lines of code counts.

Yes, pygount provides JSON output for codebase metrics and LOC counts. The codebase analysis optionally produces JSON formatted results containing summary metrics, file counts, and per-language breakdowns for integration into other tools.

The best way to measure repo size and language balance is to analyze codebases with LOC counting and language distribution metrics. These repo health indicators quantify file counts and language composition to inform refactoring decisions.

Unexpected language composition results during codebase analysis often occur when noisy directories are included. Excluding build artifacts and vendor files from your repository before running pygount ensures the language breakdown reflects your actual source code.