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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".