Feasibility of model checking software requirements: a case study
Bibliographic record
Abstract
Model checking is an effective technique for verifying properties of a finite specification. A model checker accepts a specification and a property, and it searches the reachable states to determine if the property is a theorem of the specification. Because model checking examines every state of the specification, it is a more thorough validation technique than testing executable specifications. However, some researchers question the feasibility of model checking, because the size of a specifications state-space grows exponentially with respect to the number of variables in the specification. This paper demonstrates the feasibility of symbolically model checking a non-trivial specification: the software requirements of the A-7E aircraft. The A-7E requirements document lists five properties that the designers manually derived from the requirements. Using McMillan's (1992) Symbolic Model Verifier, we were able to verify or find a counterexample to each property in less than 10-15 CPU minutes. In particular, we found that an important safety property did not hold.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".