Principles of Discrete Event System Specification model verification
Bibliographic record
Abstract
Real-time systems modeling and verification is a complex task. In many cases, formal methods have been employed to deal with the complexity of these systems, but checking those models is usually unfeasible. Modeling and simulation methods introduce a means of validating these model’s specifications. In particular, Discrete Event System Specification (DEVS) models can be used for this purpose. Here, we introduce a new extension to the DEVS formalism, called the Rational Time-Advance DEVS (RTA-DEVS), which permits modeling the behavior of real-time systems that can be modeled by the classical DEVS; however, RTA-DEVS models can be formally checked with standard model-checking algorithms and tools. In order to do so, we introduce a procedure to create timed automata (TA) models that are behaviorally equivalent to the original RTA-DEVS models. This enables the use of the available TA tools and theories for formal model checking. Further, we introduce a methodology to transform classic DEVS models to RTA-DEVS models, thus enabling formal verification of classic DEVS with an acceptable accuracy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".