The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse
Bibliographic record
Abstract
In order to spot defect-introducing code changes during review before they are integrated into a project's version control system, a variety of defect prediction models have been designed. Most of these models focus exclusively on source code properties, like the number of added or deleted lines, or developer-related measures like experience. However, a code change is only the outcome of a much longer process, involving discussions on an issue report and review discussions on (different versions of) a patch. % Ignoring the characteristics of these activities during prediction is unfortunate, since Similar to how body language implicitly can reveal a person's real feelings, the length, intensity or positivity of these discussions can provide important additional clues about how risky a particular patch is or how confident developers and reviewers are about the patch. In this paper, we build logistic regression models to study the impact of the characteristics of issue and review discussions on the defect-proneness of a patch. Comparison of these models to conventional source code-based models shows that issue and review metrics combined improve precision and recall of the explanatory models up to 10%. Review time and issue discussion lag are amongst the most important metrics, having a positive (i.e., increasing) relation with defect-proneness.
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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 | 0.026 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".