Languages statistics πŸ“Š

The following table provides a comprehensive breakdown of the codebase by language. It includes the number of files, lines of code, comments, and blank lines for each language used in the repository.

Statistics πŸ“ˆ

Deploy directory πŸš€

=============================================================================== Language Files Lines Code Comments Blanks =============================================================================== HCL 86 3001 2477 183 341 INI 1 5 5 0 0 JSON 16 34729 34729 0 0 Pan 2 139 128 0 11 PHP 3 76 76 0 0 Shell 18 1559 1208 85 266 Plain Text 81 1014 0 1014 0 YAML 416 62615 61692 695 228 =============================================================================== Total 623 103138 100315 1977 846 ===============================================================================

All project ♾️

=============================================================================== Language Files Lines Code Comments Blanks =============================================================================== BASH 10 260 200 23 37 Dockerfile 1 17 10 5 2 Gherkin (Cucumber) 2 49 46 0 3 Go 3 463 432 0 31 HCL 90 3202 2643 190 369 INI 1 5 5 0 0 JavaScript 192 12232 10576 848 808 JSON 81 258111 258111 0 0 Nix 12 640 621 6 13 Pan 2 139 128 0 11 PHP 4 96 96 0 0 Shell 31 2533 2012 142 379 Plain Text 87 1244 0 1192 52 TOML 2 52 39 2 11 YAML 525 68555 67559 722 274 ------------------------------------------------------------------------------- Markdown 62 13107 0 6914 6193 |- BASH 6 19 19 0 0 |- JavaScript 1 13 13 0 0 |- Markdown 1 1 0 1 0 (Total) 13140 32 6915 6193 =============================================================================== Total 1105 360705 342478 10044 8183 ===============================================================================

Purpose of the language statistics πŸ’‘

Understanding the distribution of languages within a codebase proves essential for several reasons:

  1. Code Maintenance: Different languages require different maintenance efforts. Knowing the proportion of each language helps in resource allocation.
  2. Skill Management: It assists in identifying the skill set needed for the team. For instance, a high number of JavaScript files indicates the need for more JavaScript developers.
  3. Code Quality: Monitoring the lines of code and comments can help in maintaining code quality. A good balance between code and comments indicates well-documented code.
  4. Project Planning: It aids in planning future development. For example, focus on enhancements in yaml handling tools if a project relies on yaml files.
  5. Technical Debt: High numbers in certain languages might signal potential areas of technical debt that need addressing, such as legacy systems predominantly written in a specific language.