Bibliographic record
Abstract
Bug triaging is the process that consists in screening and prioritising bugs to allow a software organisation to focus its limited resources on bugs with high impact on software quality. In a previous work, we proposed an entropy-based crash triaging approach that can help software organisations identify crash-types that affect a large user base with high frequency. We refer to bugs associated to these crash-types as highly-impactful bugs. The proposed triaging approach can identify highly-impactful bugs only after they have led to crashes in the field for a certain period of time. Therefore, to reduce the impact of highly-impactful bugs on user perceived quality, an early identification of these bugs is necessary. In this paper, we examine the characteristics of highly-impactful bugs in Mozilla Firefox and Fennec for Android, and propose statistical models to help software organisations predict them early on before they impact a large population of users. Results show that our proposed prediction models can achieve a precision up to 64.2% (in Firefox) and a recall up to 98.3% (in Fennec). We also evaluate the benefits of our proposed models and found that, on average, they could help reduce 23.0% of Firefox' crashes and 13.4% of Fennec's crashes, while reducing 28.6% of impacted machine profiles for Firefox and 49.4% for Fennec. Software organisations could use our prediction models to catch highly-impactful bugs early during the triaging process, preventing them from impacting a larger user base.
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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 | 0.008 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".