Classifying field crash reports for fixing bugs: A case study of Mozilla Firefox
Bibliographic record
Abstract
Many software systems support automatic collection of field crash-reports which record the stack traces and other runtime information when crashes occur. Analysis of field crash-reports can help developers to locate and fix bugs. However, the amount of crash-reports collected is often too large to handle. To reduce the amount of data for the analysis, the existing approaches group similar crash-reports together. A bug can trigger a crash in different usage scenarios. Therefore, the crash-reports triggered by the same bug may not be identical. Using the existing approaches, the crash-reports triggered by the same bugs can be distributed into different groups and one group may contain crash-reports triggered by different bugs. We perform an empirical study of crash-reports collected for Mozilla Firefox to analyze the impact of crash-report grouping and identify the characteristics of an efficient grouping. We observe that when a group contains crash-reports triggered by multiple bugs, it takes longer time to fix the bugs in comparison to the bugs where crash-reports triggered by each bug are grouped separately. To effectively reduce the bug fixing time, we propose a grouping approach, such that, each group contains the crash-reports triggered by only one bug. The case study shows that an effective grouping can reduce the bug fix time by more than 5%.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".