Towards integrated verification of timed transition models
Bibliographic record
Abstract
Abstract. This paper describes an attempt to combine theorem proving and model-checking to formally verify real-time systems in a discrete time setting. The Timed Automata Modeling Environment (TAME) has been modified to provide a formal model for Time Transition Models (TTMs) in the PVS proof checker. Strong and weak state-event observation equivalences are formalized in PVS for state-event labeled transition systems (SELTS), the underlying semantic model of TTMs. The state-event equivalences form the basis of truth value preserving abstractions for a real-time temporal logic. When appropriate restrictions are placed upon the TTMs, their PVS models can be easily translated into input for the SAL model-checker. A simple real-time control system is specified and verified using these theories. While these preliminary results indicate that the combination of PVS and SAL could provide a useful environment to perform equivalence verification, model-checking and compositional model reduction of real-time systems, the current implementation in the general purpose SAL model-checker lags well behind state of the art real-time model-checkers. Keywords: Real-time, equivalence verification, theorem proving, PVS, model-checking, model reduction, SAL
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".