An Entropy Evaluation Approach for Triaging Field Crashes: A Case Study of Mozilla Firefox
Bibliographic record
Abstract
A crash is an unexpected termination of an application during normal execution. Crash reports record stack traces and run-time information once a crash occurs. A group of similar crash reports represents a crash-type. The triaging of crash-types is critical to shorten the development and maintenance process. Crash triaging process decides the priority of crash-types to be fixed. The decision typically depends on many factors, such as the impact of the crash-type, (i.e, its severity), the frequency of occurring, and the effort required to implement a fix for the crash-type. In this paper, we propose the use of entropy region graphs to triage crash-types. An entropy region graph captures the distribution of the occurrences of crash-types among the users of a system. We conduct an empirical study on crash reports and bugs, collected from 10 beta releases of Fire fox 4. We show that our proposed triaging technique enables a better classification of crash-types than the current triaging used by Fire fox teams. Developers and managers could use such a technique to prioritize crash-types during triage, to estimate developer workloads, and to decide which crash-types patches should be included in a next release.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".