Understanding the Impact of Domain Term Explanation on Duplicate Bug Report Detection
Bibliographic record
Abstract
Duplicate bug reports make up 42% of all reports in bug tracking systems (e.g., Bugzilla), causing significant maintenance overhead. Hence, detecting and resolving duplicate bug reports is essential for effective issue management. Traditional techniques often focus on detecting textually similar duplicates. However, existing literature has shown that up to 23% of the duplicate bug reports are textually dissimilar. Moreover, about 78% of bug reports in open-source projects are very short (e.g., less than 100 words) often containing domain-specific terms or jargon, making the detection of their duplicate bug reports difficult. In this paper, we conduct a large-scale empirical study to investigate whether and how enrichment of bug reports with the explanations of their domain terms or jargon can help improve the detection of duplicate bug reports. We use 92,854 bug reports from three open-source systems, replicate seven existing baseline techniques for duplicate bug report detection, and answer two research questions in this work. We found significant performance gains in the existing techniques when explanations of domain-specific terms or jargon were leveraged to enrich the bug reports. Our findings also suggest that enriching bug reports with such explanations can significantly improve the detection of duplicate bug reports that are textually dissimilar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".