Feasibility of model checking software requirements: a case study
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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