Validating Assertion Language Rewrite Rules and Semantics With Automated Theorem Provers
Bibliographic record
Abstract
Modern assertion languages such as property specification language (PSL) and SystemVerilog assertions include many language constructs. By far, the most economical way to process the full languages in automated tools is to rewrite the majority of operators to a small set of base cases, which are then processed in an efficient way. Since recent rewrite attempts in the literature have shown that the rules could be quite involved, sometimes counterintuitive, and that they can make a significant difference in the complexity of interpreting assertions, ensuring that the rewrite rules are correct is a major contribution toward ensuring that the tools are correct, and even that the semantics of the assertion languages are well founded. This paper outlines the methodology for computer-assisted proofs of several publicly known rewrite rules for PSL properties. We first present the ways to express the PSL syntax and semantics in the prototype verification system (PVS) theorem prover, and then prove or disprove the correctness of over 50 rewrite rules published without proofs in various sources in the literature. In doing so, we also demonstrate how to circumvent known issues with PSL semantics regarding the${\ssr never}$and${\ssr eventually}!$operators, and offer our proposals on assertion language semantics.
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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 | 0.027 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".