Generalizing the DS-Methods for Testing Non-Deterministic FSMs
Bibliographic record
Abstract
There exists a significant body of work devoted to so-called complete tests which guarantee the detection of all the faults in a given fault domain. Several methods for generating complete tests for finite state machines (FSMs) which are based on a distinguishing sequence (DS) have been proposed. These methods even if extended to use adaptive DSs apply only to deterministic FSMs and the question arises whether they can be extended to non-deterministic FSMs to test for trace inclusion. In this paper, we generalize the notion of DS to a so-called total state separator, which is an adaptive experiment distinguishing states in any FSM that is trace included into the specification FSM. We then propose a method to test non-deterministic FSMs for trace inclusion. State separator is a key means of the proposed method, which has two phases: in the first phase, a preset test is constructed, which should be repeatedly applied to a non-deterministic implementation, thus requiring its resetting; in the second phase, the implementation is tested online and no reset is required. To the best of our knowledge, this is the first method which tests non-deterministic FSMs for the trace inclusion conformance relation, while avoiding resetting implementations to re-execute tests for transition verification.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".