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        13072            0         6899         6173
 |- BASH                 6           19           19            0            0
 |- JavaScript           1           13           13            0            0
 |- Markdown             1            1            0            1            0
 (Total)                          13105           32         6900         6173
===============================================================================
 Total                1105       360670       342478        10029         8163
===============================================================================

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.