Transformer Models for Automated Bug Triaging and Duplicate Bug Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it