A Machine Learning Approach to Improve the Detection of CI Skip Commits

IEEE Transactions on Software Engineering(2021)

引用 49|浏览15
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摘要
Continuous integration (CI) frameworks, such as Travis CI, are growing in popularity, encouraged by market trends towards speeding up the release cycle and building higher-quality software. A key facilitator of CI is to automatically build and run tests whenever a new commit is submitted/pushed. Despite the many advantages of using CI, it is known that the CI process can take a very long time to c...
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Machine learning,Decision trees,Feature extraction,Message systems,Documentation,Buildings
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