Bibliographic record
Abstract
Automated debugging tools based on Boolean Satisfiability (SAT) have greatly alleviated the time and effort required to diagnose and rectify a failing design. Practical experience shows that long-running debugging instances can often be resolved faster using partial results that are available before the SAT solver completes its search. In such cases it is preferable for the tool to maximize the number of suspects it returns during the early stages of its deployment. To capitalize on this observation, this paper proposes a directed SAT-based debugging algorithm which prioritizes examining design locations that are more likely to be suspects. This prioritization is determined by suspect2vec --- a model which learns from historical debug data to predict the suspect locations that will be found. Experiments show that this algorithm is expected to find 16% more suspects than the baseline algorithm if terminated prematurely, while still retaining the ability to find all suspects if executed to completion. Key to its performance and a contribution of this work is the accuracy of the suspect prediction model. This is because incorrect predictions introduce overhead in exploring parts of the search space where few or no solutions exist. Suspect2vec is experimentally demonstrated to outperform existing suspect prediction methods by an average accuracy of 5--20%.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".