Timed test cases generation based on state characterization technique
Bibliographic record
Abstract
Real time reactive systems interact with their environment, using inputs and outputs, within specified time constraints. For such systems, a functional misbehavior or a deviation from the specified time constraints may have catastrophic consequences. Therefore, ensuring the correctness of real time systems becomes very important. We introduce the potential faults which can be encountered in a timed system implementation. We adapt an existing test cases generation technique, based on state characterization set, to generate timed test cases from a timed system specification. We model a timed system with a Timed Input Output Automaton (TIOA), which is a variant of the Alur and Dill model (R. Alur and D. Dill, 1994). In order to generate the timed test suite, the TIOA is first transformed into a Nondeterministic Timed Finite State Machine (NTFSM) with a given granularity. We illustrate our method with an example.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".