Fault Detection in Timed FSM with Timeouts by SAT-Solving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Faults in safety critical real-time systems are not only logical, but they can correspond to violations of timing constraints. They must be detected to avoid system failures with adverse consequences. Developing efficient fault detection techniques for varieties of system models is still challenging. In this paper, we deal with fault detection for timed finite state machines with timeouts (TFSMs-T). TFSM-T is an extension of FSM to model timing constraints in safety-critical real-time systems. We lift a fault detection approach developed for FSM to generate tests detecting both logical faults and violations of time constraints in TFSMs-T. The approach is based on constraint solving and uses mutation machines to represent domains of faulty implementations (mutants) of a specification TFSMs-T. It also avoids enumerating the implementations one by one. We develop a prototype tool and we conduct experiments to evaluate the scalability of the proposed methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it