Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization
Bibliographic record
Abstract
Continuous Integration (CI) systems aim to provide quick feedback on the success of the code changes by keeping on building the entire systems upon code changes are committed. However, building the entire software system is usually resource and time consuming. Thus, build outcome prediction is usually employed to distinguish the successful builds from the failed ones to cut the building efforts on those successful builds that do not result in any immediate action of the developer. Nevertheless, build outcome prediction in CI is challenging since the learner should be able to learn from a stream of build events with and without the build outcome labels and provide immediate prediction on the next build event. Also, the distribution of the successful and the failed builds are often highly imbalanced. Unfortunately, the existing methods fail to address these challenges well. In this paper, we address these challenges by proposing a semi-supervised online AUC optimization method for CI build outcome prediction. Experiments indicate that our method is able to cut the software building efforts by effectively identify the successful builds, and it outperforms the existing methods that elaborate to address part of these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".