Source Transformation for Concurrency Analysis
Bibliographic record
Abstract
Concurrent programming poses a unique set of problems for quality assurance. These difficulties include the complexities of deadlock, livelock and divergence, which can be extremely difficult to detect and debug. A variety of tools have been developed to assist designers and developers of concurrent applications. Some of these tools, such as VeriSoft, are specific to particular implementation languages, such as C++. The Java Remote Method Invocation (Java RMI) package facilitates the implementation of concurrent applications, including those where processes reside on different hosts and communicate over networks. Unfortunately, it does not relieve the developer from the potential pitfalls of controlling concurrent access to remote objects, and may, in fact, make concurrency problems even more difficult to find. This paper presents an approach that allows the VeriSoft state exploration system to be used to analyze Java RMI programs for deadlock, livelock, divergence, and assertion violations. The system works by transforming Java RMI programs into C++ programs where Java syntax, structure, concurrency and memory management are replaced by C++ equivalents and Java RMI communication has been transformed to VeriSoft C++ inter-process communication. We present the details of this transformation and discuss preliminary results for a number of small examples.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".