A Fully Automated Approach to Discovering Nondeterminism in State Machine Diagrams
Why this work is in the frame
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Bibliographic record
Abstract
We present a fully automated technique to detect nondeterminism in state diagrams. Although nondeterminism is a tool often adopted by requirement engineers for specification of a system under development (SUD), it is normally undesirable in actual implementation. Discovering nondeterminism manually is infeasible for industrial-sized systems. Solutions in the literature lack the capability to analyze infinite-state systems. We leverage the nuXmv model checker to analyze unbounded domains and implement an algorithm that systematically computes a minimal set of comparable transitions for the SUD yet eliminates false positives by model checking. To validate our approach, we analyze a real-world system and report discovered cases of nondeterminism. We employ Umple's capability to convert state machines to nuXmv.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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