Verification of parameterized concurrent programs by modular reasoning about data and control
Bibliographic record
Abstract
In this paper, we consider the problem of verifying thread-state properties of multithreaded programs in which the number of active threads cannot be statically bounded. Our approach is based on decomposing the task into two modules, where one reasons about data and the other reasons about control. The data module computes thread-state invariants (e.g., linear constraints over global variables and local variables of one thread) using the thread interference information computed by the control module. The control module computes a representation of thread interference, as an incrementally constructed data flow graph, using the data invariants provided by the data module. These invariants are used to rule out patterns of thread interference that can not occur in a real program execution. The two modules are incorporated into a feedback loop, so that the abstractions of data and interference are iteratively coarsened as the algorithm progresses (that is, they become weaker) until a fixed point is reached. Our approach is sound and terminating, and applicable to programs with infinite state (e.g., unbounded integers) and unboundedly many threads. The verification method presented in this paper has been implemented into a tool, called Duet. We demonstrate the effectiveness of our technique by verifying properties of a selection of Linux device drivers using Duet, and also compare Duet with previous work on verification of parameterized Boolean program using the Boolean abstractions of these drivers.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".