Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study
Bibliographic record
Abstract
This paper partially replicates prior works on building historical commits [1], commit risk modeling [2], and a comparison of statistical bug models and static bug finders [3].We examine 8 Maven-based projects with an average lifespan of 5.8 years. To historically build these projects across a total of 45k commits, we develop a series of techniques, such as flexibly selecting the version of a library that is closest to the commit date. We are able to build a per project average of 78.4% of all commits, a doubling in buildability compared to prior work. We also develop a git blame strategy to assign warnings even when a commit does not build.We run JLint and FindBugs and create a logistic regression model to predict if a commit that introduces a warning has higher odds of introducing a bug. The static bug finders model accounted for only 13% of the deviance, while the statistical bug model accounted for 19.5%. We had expected static bug finder warnings to improve the predictive power of models of bug introducing changes, but we clearly attained a negative result.To understand this negative result, we perform a preliminary user study of developers who introduced new warnings in 37 projects. We found that while warnings might not predict bugs, 53% and 21% of warnings in Findbugs and Jlint respectively are useful. We also study whether just-in-time warnings presentation on each commit impacted usefulness. We find that the later a warning is shown to a developer, the less useful it is perceived to be (a median of 11.5 days versus 23 days for non useful warnings).Based on our findings, we modify the existing COMMITGURU interface to add new warnings to the specific line in a changed file. The empirical study data [4] and WARNINGSGURU tool [5] are publicly available.
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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 | 0.028 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".