CrashAutomata: an approach for the detection of duplicate crash reports based on generalizable automata
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| 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 it