Predicting Method Crashes with Bytecode Operations
Bibliographic record
Abstract
Software monitoring systems have high performance overhead because they typically monitor all processes of the running program. For example, to capture and replay crashes, most current systems monitor all methods; thus yielding a significant performance overhead. Lowering the number of methods being monitored to a smaller subset can dramatically reduce this overhead. We present an approach that can help arrive at such a subset by reliably identifying methods that are the most likely candidates to crash in a future execution of the software. Our approach involves learning patterns from features of methods that previously crashed to classify new methods as crash-prone or non-crash-prone. An evaluation of our approach on two large open source projects, ASPECTJ and ECLIPSE, shows that we can correctly classify crash-prone methods with an accuracy of 80--86%. Notably, we found that the classification models can also be used for cross-project prediction with virtually no loss in classification accuracy. In a further experiment, we demonstrate how a monitoring tool, RECRASH could take advantage of only monitoring crash-prone methods and thereby, reduce its performance overhead and maintain its ability to perform its intended tasks.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".