Towards a better collaboration of static and dynamic analyses for testing concurrent programs
Bibliographic record
Abstract
Testing concurrent programs remains a difficult task due to the non-deterministic nature of concurrent executions. Many approaches have been proposed to combine static and dynamic analysis to reduce the complexity of uncovering potential concurrency bugs. However, the existing collaboration schemes only provide a limited mechanism for exchanging relevant information between the two analyses. For example, alias information only flows from the static analysis module to the dynamic analysis module at the beginning of the dynamic analysis. Therefore, we cannot fully exploit the advantages of each type of analysis. Motivated by this observation, in this paper we present a new testing technique which enables a tighter collaboration between static analysis and dynamic analysis. In this collaboration scheme, static analysis and dynamic analysis interact iteratively throughout the whole testing process. Static analysis uses coarse-grained analysis to guide the dynamic analysis to concentrate on the relevant search space, while dynamic analysis collects concrete runtime information during the guided exploration. The runtime information provided by the dynamic analysis helps the static analysis to refine its coarse-grained analysis and provides better guidance on dynamic analysis. Currently, our implementation consists of a static analysis module based on Soot and a dynamic analysis module based on JPF (Java PathFinder).
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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 | 0.055 | 0.099 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".