Cone of influence and constants propgation reduction techniques for MDG model checker
Bibliographic record
Abstract
Sizes and complexity of modern design models has become the main challenges that can limit the model checking process due to the state explosion problem. Applying reduction techniques on complex modern system models to reduce their sizes, obtain relevant parts, and basically constructing such simplification for the model checking process can lead to verify those complex models. While Multiway Decision Graphs model checker (MDG-MC) has great advantages of using abstract variables and uninterpreted function symbols to describe sets of states and transition relations that increase the functional domain of MDG-MC, the state explosion problem is still the main limitation that prevents MDG-MC from verifying real modern designs. In this paper, and to alleviate the state explosion problem, we introduce a simple but powerful Cone of Influence and constants propagation reduction techniques to improve the efficiency of verification process of MDG-MC. The preliminary experimental results confirm that the reduction in model checking time and memory size can be dramatic, thereby allowing for the verification of hitherto intractable systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".