Toward a Rigorous Approach for Verifying Cyber-Physical Systems Against Requirements
Bibliographic record
Abstract
Verifying that complex cyber-physical systems such as power plants satisfy the requirements that ensure their proper operation, in particular with respect to safety, dependability, and environmental regulations, is difficult due to the large number of potential situations to be explored in terms of initiating events and their chain of consequences on the behavior of the system. This paper presents a new framework for supporting a methodology that aims at reconciling innovation (ability to explore many different solutions) and safety (ability to avoid unacceptable behavior). The general principle is to produce independently formal models of the requirements, of the possible variants of the design, and of the dynamic behavior of the system for the possible designs, then assemble them together to simulate the full system's behavior to automatically detect possible violations of the requirements.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".