CrashAutomata: an approach for the detection of duplicate crash reports based on generalizable automata
Bibliographic record
Abstract
Crash reporting systems are useful tools that allow users to report system failures, subsequently contacting the appropriate support group for resolution. As a software system grows and becomes more versatile, the number of crashes increases. A large software company receives typically thousands of crashes a day, which make it difficult for software engineers to address these reports in a timely manner. Fortunately, not all reports are new; many of them are duplicates of previously reported crashes. Research has shown that early detection of duplicate reports can reduce the effort and time it takes to handle crash reports. In this paper, we propose a new approach for detecting duplicate crash reports, called CrashAutomata. CrashAutomata builds a model from historical crash reports (more precisely their stack traces) that is used to classify an incoming report. The model is based on varied-length n-grams and automata. Unlike existing techniques, CrashAutomata takes advantage of the generalization aspect of automata, making it possible to build a representative model of crash reports, reducing the number of false positives. When applied to crash reports of the Firefox system, CrashAutomata results in very high precision and recall. It also outperforms CrashGraph, a leading technique in the detection of duplicate crash reports.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".