Detecting duplicate bug reports with software engineering domain knowledge
Bibliographic record
Abstract
Bug deduplication, ie, recognizing bug reports that refer to the same problem, is a challenging task in the software‐engineering life cycle. Researchers have proposed several methods primarily relying on information‐retrieval techniques. Our work motivated by the intuition that domain knowledge can provide the relevant context to enhance effectiveness, attempts to improve the use of information retrieval by augmenting with software‐engineering knowledge. In our previous work, we proposed the software‐literature‐context method for using software‐engineering literature as a source of contextual information to detect duplicates. If bug reports relate to similar subjects, they have a better chance of being duplicates. Our method, being largely automated, has a potential to substantially decrease the level of manual effort involved in conventional techniques with a minor trade‐off in accuracy. In this study, we extend our work by demonstrating that domain‐specific features can be applied across projects than project‐specific features demonstrated previously while still maintaining performance. We also introduce a hierarchy‐of‐context to capture the software‐engineering knowledge in the realms of contextual space to produce performance gains. We also highlight the importance of domain‐specific contextual features through cross‐domain contexts: adding context improved accuracy; Kappa scores improved by at least 3.8% to 10.8% per project.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".