Transformer Models for Automated Bug Triaging and Duplicate Bug Detection
Bibliographic record
Abstract
In the software engineering field, developer teams must handle bug reports of varying sources and formats to maintain and optimize software applications as issues arise. A team's workflow for handling bugs involves multiple stages to review, assess, assign, and resolve bugs. In teams for large-scale applications, streamlining such processes is vital for efficient operations as they are exposed to greater volumes and varieties of bug reports. This thesis focuses on bug triaging and duplicate bug detection as preliminary processing options for the automated bucketing and assignment of bugs. In the bug triaging task, Transformer-based models are found to outperform in mean Rank-5, Rank-10, and Mean Reciprocal Rank across several open-source datasets for various software projects. In the duplicate bug detection task, similarity learning is employed and Transformer-based siamese models with domain adaptation are shown to improve similarity learning capabilities with improvements in mean Area under the Curve, Recall-rate @ k, and Mean Reciprocal Rank performance.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".