{"id":22430016,"url":"https://github.com/brevex/code-complexity-data-analisis","last_synced_at":"2026-04-29T18:35:00.891Z","repository":{"id":212005554,"uuid":"730480584","full_name":"Brevex/Code-Complexity-Data-Analisis","owner":"Brevex","description":"Data collection that shows different complexity scores in an algorithmic dataframe.","archived":false,"fork":false,"pushed_at":"2024-02-28T18:53:21.000Z","size":114,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T07:14:04.726Z","etag":null,"topics":["code-analysis","data-analysis","data-science","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Brevex.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-12-12T02:42:00.000Z","updated_at":"2023-12-13T02:55:40.000Z","dependencies_parsed_at":"2023-12-12T05:22:06.263Z","dependency_job_id":"b2834a6f-07b1-4ef5-b6f1-37ec04b3d162","html_url":"https://github.com/Brevex/Code-Complexity-Data-Analisis","commit_stats":null,"previous_names":["brevex/complexity","brevex/code-metric-data-analisis","brevex/code-complexity-data-analisis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Brevex/Code-Complexity-Data-Analisis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FCode-Complexity-Data-Analisis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FCode-Complexity-Data-Analisis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FCode-Complexity-Data-Analisis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FCode-Complexity-Data-Analisis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Brevex","download_url":"https://codeload.github.com/Brevex/Code-Complexity-Data-Analisis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FCode-Complexity-Data-Analisis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32439295,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T18:12:22.909Z","status":"ssl_error","status_checked_at":"2026-04-29T18:11:33.322Z","response_time":110,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["code-analysis","data-analysis","data-science","python"],"created_at":"2024-12-05T21:06:48.618Z","updated_at":"2026-04-29T18:35:00.885Z","avatar_url":"https://github.com/Brevex.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align = \"center\"\u003e Code Complexity Data Analisis \u003c/h1\u003e\u003cbr\u003e\n\n\u003ch2\u003e \u0026#128269; About the project \u003c/h2\u003e\u003cbr\u003e\n\n\u003cp\u003eThis data analysis aims to catalog and filter different algorithms into a complexity metric. An initial dataframe with the scores \nwas filtered to better understand how efficient each algorithm in the database can be. The final dataframe with the filtered data \nclassifies the algorithms into 3 labels: Risk by Cyclomatic Complexity, Fan-in and Fan-out Complexity and Maintainability Score.\u003c/p\u003e\u003cbr\u003e\n\n\u003ch2\u003e \u0026#128302; Technologies Used \u003c/h2\u003e\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/syvixor/skills-icons\"\u003e\n\t  \u003cimg src=\"https://skills.syvixor.com/api/icons?i=python\" alt=\"Skills\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003ch2\u003e \u0026#128200; Categorization Criteria \u003c/h2\u003e\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \n  | Type of Complexity            | Equation                                                    |\n  |:-----------------------------:|:-----------------------------------------------------------:|\n  | Fan-in and Fan-out            | $C = wmc \\cdot (Fin \\cdot Fout)^2$                          |\n  | Maintainability Score         | $MS = loopQty + comparisonsQty + numbersQty + variablesQty$ |\n  | Risk by Cyclomatic Complexity | $wmc$                                                       |\n  \n  | Type of Complexity            | Appraisal Criteria                                          | \n  |:-----------------------------:|:-----------------------------------------------------------:|\n  | Fan-in and Fan-out            | $0=(\u003c= 100), 1=(101-1000), 2=(\u003e 1000)$                      |\n  | Maintainability Score         | $0=(\u003c= 65), 1=(66-85), 2=(\u003e 85)$                            |\n  | Risk by Cyclomatic Complexity | $0=(\u003c= 10), 1=(11-20), 2=(21-50), 3=(\u003e 50)$                 |\n  \n  | Evaluation value   | Risk by Cyclomatic Complexity | Fan-in and Fan-out Complexity | Maintainability Score |\n  |:------------------:|:-----------------------------:|:-----------------------------:|:---------------------:|\n  | 0                  | Low                           | Good                          | Good                  |\n  | 1                  | Moderate                      | Moderate                      | Moderate              |\n  | 2                  | High                          | High                          | Bad                   |\n  | 3                  | Very High                     | (N/A)                         | (N/A)                 |\n  \n\u003c/div\u003e\n\n\u003cbr\u003e\u003ch2\u003e \u0026#128202; Analysis Result \u003c/h2\u003e\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Brevex/Code-Metric-Data-Analisis/blob/84451405bfd9952321e6ecb98d76beded072cad5/readme%20images/chart.png\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\u003ch3 align = \"center\"\u003e - By \u003ca href = \"https://www.linkedin.com/in/breno-barbosa-de-oliveira-810866275/\" target = \"_blank\"\u003eBreno\u003c/a\u003e - \u003c/h3\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrevex%2Fcode-complexity-data-analisis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrevex%2Fcode-complexity-data-analisis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrevex%2Fcode-complexity-data-analisis/lists"}