A DESIGN FOR VERIFICATION APPROACH USING AN EMBEDDING OF PSL IN AsmL
Bibliographic record
Abstract
In this paper, we propose to integrate an embedding of Property Specification Language (PSL) in Abstract State Machines Language (AsmL) with a top–down design for verification approach in order to enable the model checking of large systems at the early stages of the design process. We provide a complete embedding of PSL in the ASM language AsmL, which allows us to integrate PSL properties as a part of the design. For verification, we propose a technique based on the AsmL tool that translates the code containing both the design and the properties into a finite state machine (FSM) representation. We use the generated FSM to run model checking on an external tool, here SMV. Our approach takes advantage of the AsmL language capabilities to model designs at the system level as well as from the power of the AsmL tool in generating both C# code and FSMs from AsmL models. We applied our approach on the PCI-X bus standard, which AsmL model was constructed from the informal standard specifications and a subsequent UML model. Experimental results on the PCI-X bus case study showed a superiority of our approach to conventional verification.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".