Bibliographic record
Abstract
Concurrent programs are difficult to debug and verify because of the nondeterministic nature of concurrent executions. A particular concurrency-related bug may only show up under certain rarely-executed thread interleavings. Therefore, commonly used debugging methodologies, such as inserting print statements, are no longer sufficient for uncovering concurrency-related bugs. However, many existing bug detection methods, such as dynamic analysis and model checking, have a very high computational cost. In this paper, we introduce a new technique for uncovering concurrency-related bugs from multithreaded Java programs. Our technique uncovers concurrency-related bugs by generating and testing read-write assignment sequences, referred to as value schedules, of a multithreaded Java program. Our value-schedule-based technique distinguishes itself in its ability to avoid exploring superfluous program state space caused by speculative permutation on transitions. Therefore, our technique can achieve a higher degree of POR (Partial Order Reduction) than existing methods. We demonstrate our technique using some programs, with an implementation built using an explicit state model checker called JPF
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".