Source-level transformations for improved formal verification
Bibliographic record
Abstract
A major obstacle to widespread acceptance of formal verification is the difficulty in using the tools effectively. Although learning the basic syntax and operation of a formal verification tool may be easy, expert users are often able to accomplish a verification task while a novice user encounters time-out or space-out attempting the same task. In this paper, we assert that often a novice user will model a system in a different manner-semantically equivalent, but less efficient for the verification tool-than an expert user would, that some of these inefficient modeling choices can be easily detected at the source-code level, and that a robust verification tool should identify these inefficiencies and optimize them, thereby helping to close the gap between novice and expert users. To test our hypothesis, we propose some possible optimizations for the Mur/spl phi/ verification system, implement the simplest of these, and compare the results on a variety of examples written by both experts and novices (the Mur/spl phi/ distribution examples, a set of cache coherence protocol models, and a portion of the IEEE 1394 Firewire protocol). The results support our assertion-a nontrivial fraction of the Mur/spl phi/ models written by novice users were significantly accelerated by the very simple optimization. Our findings strongly support further research in this area.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".