Verification of real time controllers against timing diagram specifications using constraint logic programming
Bibliographic record
Abstract
Given a pseudo-synchronous (sampled input) finite-state machine (FSM) implementation of a real-time controller and a specification in the form of timing diagrams (TDs), the question we wish to answer is whether the controller satisfies the specification. The method we propose uses constraint logic programming (CLP) based on relational interval arithmetic (RIA) and domain narrowing. The controller FSM is fed with input sequences derived from the timing assumptions on the inputs as stated in the specification, and its outputs are verified against the required timing of the specification (the commit constraints). Since timing constraints in TD specifications usually involve intervals of possible values, there may be many input sequences satisfying the assumptions. We consider all possible input sequences in one symbolic execution of the machine derived from the TD and the controller by formulating the execution of the machine as consistency checking of a constraint system. The number of constraints checked is linear with the sum of the lengths of the intervals of the assumption constraints in the specification. It was implemented in CLP(BNR) Prolog.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".