Prediction of Bug Inducing Commits Using Metrics Trend Analysis
Bibliographic record
Abstract
Continuous software engineering advocates a release-small, release-often process model, where new functionality is added to a system very frequently and in small increments. In such a process model, it is important to be able to identify as early as possible, and every time a change is introduced, whether the system has entered a state where faults are more likely to occur. In this paper, we present a method that is based on process, quality, and source code metrics to evaluate the likelihood that an imminent bug inducing commit is highly probable. More specifically, the method analyzes the correlations, and the rate of change of selected structural and quality metrics. The findings from the SonarQube Technical Debt open-source dataset indicate that before bug inducing commits, metrics which otherwise are not corelated, suddenly exhibit a high correlation or high rate of metric value change. This metric behavior can then be used as a predictor for a imminent bug inducing commit. The technique is programing language agnostic, as it is based on metrics which are extracted without the use of specialized parsers, and can be applied to forewarn developers that a file, or a collection of files, has entered a state where faults are highly probable.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".