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ICSE 2021
Mon 17 May - Sat 5 June 2021
Tue 25 May 2021 16:40 - 17:00 at Blended Sessions Room 2 - 1.4.2. Continuous Integration Chair(s): Daniela Damian
Wed 26 May 2021 04:40 - 05:00 at Blended Sessions Room 2 - 1.4.2. Continuous Integration

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 complete. One of the core causes for such delays is the fact that some commits (e.g., cosmetic changes) unnecessarily kick off the CI process.

Therefore, the main goal of this paper is to automate the process of determining which commits can be CI skipped through the use of machine learning techniques. We first extracted 23 features from historical data of ten software repositories. Second, we conduct a study on the detection of CI skip commits using machine learning, where we built a decision tree classifier. We then examine the accuracy of using the decision tree in detecting CI skip commits. Our results show that the decision tree can identify CI skip commits with an average AUC equal to 0.89. Furthermore, the top node analysis shows that the number of developers who changed the modified files, the CI-Skip rules, and commit messages are the most important features to detect CI skip commits. Finally, we investigate the generalizability of identifying CI skip commits through applying cross-project validation, and our results show that the general classifier achieves an average of 0.74 of AUC values.

Tue 25 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

16:40 - 17:35
1.4.2. Continuous IntegrationJournal-First Papers / Technical Track / NIER - New Ideas and Emerging Results at Blended Sessions Room 2 +12h
Chair(s): Daniela Damian University of Victoria
A Machine Learning Approach to Improve the Detection of CI Skip CommitsJournal-First
Journal-First Papers
Rabe Abdalkareem Queens University, Kingston, Canada, Suhaib Mujahid Concordia University, Emad Shihab Concordia University
Link to publication DOI Pre-print Media Attached
What helped, and what did not? An Evaluation of the Strategies to Improve Continuous IntegrationArtifact ReusableTechnical TrackArtifact Available
Technical Track
Xianhao Jin Virginia Tech, Francisco Servant Virginia Tech
Pre-print Media Attached
ADEPT: A Socio-Technical Theory of Continuous IntegrationNIER
NIER - New Ideas and Emerging Results
Omar Elazhary University of Victoria, Margaret-Anne Storey University of Victoria, Neil Ernst University of Victoria, Elise Paradis University of Toronto
Pre-print Media Attached

Wed 26 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change