Path directed abstraction and refinement in SAT-based design debugging
Bibliographic record
Abstract
The past decade has seen a disproportionate amount of resources dedicated towards verification as compared to actual design. It is reported that one third of this overhead is due to the resource-intensive task of manual debugging. To relieve this burden, this work introduces the novel concept of path directed debugging within a window-based abstraction/refinement framework. The algorithm divides the error trace into non-overlapping time-windows where each window is analyzed separately. Subsequent windows are replaced with abstracted over-approximations derived from failing paths in the time domain. Using this abstracted model, each solution found is processed through an additional verification step that removes spurious solutions and simultaneously refines the problem. This paper also develops the theory that shows that the proposed approach is complete, a fact that mitigates the incompleteness inherent in past time-window based debugging methods. Experimental results on industrial designs with long error traces show a 55% decrease in peak memory usage resulting in 78% more instances being solved when compared to previous work.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".