Using Indexed and Synchronous Events to Model and Validate Cyber-Physical Systems
Bibliographic record
Abstract
Timed Transition Models (TTMs) are event-based descriptions for modelling, specifying, and verifying discrete real-time systems. An event can be spontaneous, fair, or timed with specified bounds. TTMs have a textual syntax, an operational semantics, and an automated tool supporting linear-time temporal logic. We extend TTMs and its tool with two novel modelling features for writing high-level specifications: indexed events and synchronous events. Indexed events allow for concise description of behaviour common to a set of actors. The indexing construct allows us to select a specific actor and to specify a temporal property for that actor. We use indexed events to validate the requirements of a train control system. Synchronous events allow developers to decompose simultaneous state updates into actions of separate events. To specify the intended data flow among synchronized actions, we use primed variables to reference the post-state (i.e., one resulted from taking the synchronized actions). The TTM tool automatically infers the data flow from synchronous events, and reports errors on inconsistencies due to circular data flow. We use synchronous events to validate part of the requirements of a nuclear shutdown system. In both case studies, we show how the new notation facilitates the formal validation of system requirements, and use the TTM tool to verify safety, liveness, and real-time properties.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".