Bibliographic record
Abstract
Automatic prevention and resolution of faults is an important research topic in the field of software maintenance and evolution. Existing approaches leverage code and process metrics to build metric-based models that can effectively prevent defect insertion in a software project. Metrics, however, may vary from one project to another, hindering the reuse of these models. Moreover, they tend to generate high false positive rates by classifying healthy commits as risky. Finally, they do not provide sufficient insights to developers on how to fix the detected risky commits. In this paper, we propose an approach, called CLEVER (Combining Levels of Bug Prevention and Resolution techniques), which relies on a two-phase process for intercepting risky commits before they reach the central repository. When applied to 12 Ubisoft systems, the results show that CLEVER can detect risky commits with 79% precision and 65% recall, which outperforms the performance of Commit-guru, a recent approach that was proposed in the literature. In addition, CLEVER is able to recommend qualitative fixes to developers on how to fix risky commits in 66.7% of the cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".