Bibliographic record
Abstract
Static bug detectors help improve software quality by detecting code issues (e.g., code smells or bugs). However, static bug detectors are underutilized in practice due to various reasons. One primary reason is that static bug detectors often report an overwhelming number of static warnings for one software revision. To facilitate better adoption of static bug detectors in software development, we propose a tool, namely StaticTracker, that specializes in tracking the evolution of static code warnings.StaticTracker analyzes each commit and produces the changes of static code warnings caused by the commit, i.e., a diff for static code warnings. We integrate StaticTracker in continuous integration through Git Hooks. Whenever developers push code to a git repository, StaticTracker is automatically activated to identify disappeared and newly-introduced warnings by the commit. We implement StaticTracker for two static bug detectors (Spotbugs and PMD) and evaluate StaticTracker on the recent commits of two open-source projects (Druid and Jedis). Our evaluation shows that StaticTracker is effective in reducing the overwhelming static code warnings that developers need to investigate and achieves an accuracy of 89.8%, which outperforms the state-of-the-art tracking approach with an accuracy of 68.5%.We open source the tool at https://github.com/ljj430/tracking-static-warnings_tool_demo, and the demo video is at https://www.youtube.com/watch?v=2WMOjoq1Nbs.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".