{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-machine-learning-approach-to-improve-the","title":"A Machine Learning Approach to Improve the Detection of CI Skip Commits","arxiv_id":null,"date":"2021-12-01","proceeding":"IEEE Transactions on Software Engineering, vol. 47, no. 12 2021 12","authors":["Rabe Abdalkareem","Suhaib Mujahid","Emad Shihab"],"abstract":"Continuous integration (CI) frameworks, such as Travis CI, are growing in popularity, encouraged by market trends towards\r\nspeeding up the release cycle and building higher-quality software. A key facilitator of CI is to automatically build and run tests\r\nwhenever a new commit is submitted/pushed. Despite the many advantages of using CI, it is known that the CI process can take a very\r\nlong time to complete. One of the core causes for such delays is the fact that some commits (e.g., cosmetic changes) unnecessarily\r\nkick off the CI process. Therefore, the main goal of this paper is to automate the process of determining which commits can be CI\r\nskipped through the use of machine learning techniques. We first extracted 23 features from historical data of ten software repositories.\r\nSecond, we conduct a study on the detection of CI skip commits using machine learning where we built a decision tree classifier. We\r\nthen examine the accuracy of using the decision tree in detecting CI skip commits. Our results show that the decision tree can identify\r\nCI skip commits with an average AUC equal to 0.89. Furthermore, the top node analysis shows that the number of developers who\r\nchanged the modified files, the CI-Skip rules, and commit message are the most important features to detect CI skip commits. Finally,\r\nwe investigate the generalizability of identifying CI skip commits through applying cross-project validation, and our results show that the\r\ngeneral classifier achieves an average 0.74 of AUC values.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8961089","url_pdf":"https://ieeexplore.ieee.org/abstract/document/8961089","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-machine-learning-approach-to-improve-the","repo_url":"https://github.com/debonil/skip-commit-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}