Identifying future field accesses in exhaustive state space traversal
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
One popular approach to detect errors in multi-threaded programs is to systematically explore all possible interleavings. A common algorithmic strategy is to construct the program state space on-the-fly and perform thread scheduling choices at any instruction that could have effects visible to other threads. Existing tools do not look ahead in the code to be executed, and thus their decisions are too conservative. They create unnecessary thread scheduling choices at instructions that do not actually influence other threads, which implies exploring exponentially greater numbers of interleavings. In this paper we describe how information about field accesses that may occur in the future can be used to identify and eliminate unnecessary thread choices. This reduces the number of states that must be processed to explore all possible behaviors and therefore improves the performance of exhaustive state space traversal. We have applied this technique to Java PathFinder, using the WALA library for static analysis. Experiments on several Java programs show big performance gains. In particular, it is now possible to check with Java PathFinder more complex programs than before in reasonable time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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