TraceJIT: Evaluating the Impact of Behavioral Code Change on Just-In-Time Defect Prediction
Bibliographic record
Abstract
Just-In-Time (JIT) defect prediction strives to model changes that induce future fixes so that they can be predicted or better understood to inform development practices. Prior work demonstrates that the majority of the predictive/explanatory power of JIT models derives from the size of a change (i.e., larger changes tend to be defect-prone); however, in practice, a misguided change to even a single line of code can lead to defects. While it is clearly the case that larger changes are more likely to alter the product behavior, even small changes are capable of doing this, and when they do, they pose a risk that teams should note. However, to the best of our knowledge, JIT defect prediction models are yet to incorporate features that characterize the change in product behavior when modelling risk. This paper is the first to explore the impact of behavioral code change on JIT prediction. Specifically, we propose seven dynamic features that capture the difference in product behavior before and after applying a change. These features are computed using trace logs that are collected during invocations of test suites. Using these logs, we identify which lines of code started/stopped being exercised after a change. We evaluate these features by conducting an empirical study of two large and thriving open-source projects. We observe that, compared to baseline models that use traditional features, adding our proposed set of behavior features leads to improvements of up to 5.9% of ROC-AVC, 44.8% of precision, and 14.1 % of PR-AUC. This paper not only demonstrates the importance of behavioral features for JIT defect prediction, but also lays the foundation for future work on behavioral features in other software engineering contexts, such as build outcome prediction and code reviewer recommendation.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".