Btor2-Select: Machine Learning Based Algorithm Selection for Hardware Model Checking
Bibliographic record
Abstract
Abstract In recent years, a diverse variety of hardware model-checking tools and techniques that exhibit complementary strengths and distinct weaknesses have been proposed. This state of affairs naturally suggests the use of algorithm-selection techniques to select the right tool for a given instance. To automate this process, we present Btor2-Select , a machine learning-based algorithm-selection framework for the hardware model-checking problem described in the word-level modeling language Btor2 . The framework offers an efficient and effective machine-learning pipeline for training an algorithm selector. Btor2-Select also enables the use of the trained selector to predict the most suitable off-the-shelf model checker for a given verification task and automatically invoke it to solve the task. Evaluated on a comprehensive Btor2 benchmark suite coupled with a set of state-of-the-art model checkers, Btor2-Select trained an algorithm selector that successfully closed over 65 % of the PAR-2 performance gap between the best single tool and the idealized virtual selector. Moreover, the selector outperformed a portfolio model checker that runs three complementary verification engines in parallel. Btor2-Select offers a simple, systematic, and extensible solution to harness the complementary strengths of diverse model checkers. With its fast and highly configurable training procedure, Btor2-Select can be easily integrated with new tools and applied to various application domains.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".