Improving IR-Based Bug Localization with Context-Aware Query\n Reformulation
Bibliographic record
Abstract
Recent findings suggest that Information Retrieval (IR)-based bug\nlocalization techniques do not perform well if the bug report lacks rich\nstructured information (eg relevant program entity names). Conversely,\nexcessive structured information (eg stack traces) in the bug report might not\nalways help the automated localization either. In this paper, we propose a\nnovel technique--BLIZZARD-- that automatically localizes buggy entities from\nproject source using appropriate query reformulation and effective information\nretrieval. In particular, our technique determines whether there are excessive\nprogram entities or not in a bug report (query), and then applies appropriate\nreformulations to the query for bug localization. Experiments using 5,139 bug\nreports show that our technique can localize the buggy source documents with\n7%--56% higher Hit@10, 6%--62% higher MAP@10 and 6%--62% higher MRR@10 than the\nbaseline technique. Comparison with the state-of-the-art techniques and their\nvariants report that our technique can improve 19% in MAP@10 and 20% in MRR@10\nover the state-of-the-art, and can improve 59% of the noisy queries and 39% of\nthe poor queries.\n
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".