A Machine Learning Approach to Improve the Detection of CI Skip Commits
Bibliographic record
Abstract
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 maingoalof 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 message 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 0.74 of AUC values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".