Automated debugging of missing assumptions
Bibliographic record
Abstract
Formal verification has increased efficiency by detecting corner case design bugs but it has also introduced new challenges when failures are detected. Once a counter-example is returned by a formal tool, the user typically does not know if the failure is caused by a design bug, an incorrectly written assertion, or a missing assumption. Previous work in debug automation has focused on the former two cases. This paper introduces a novel methodology to automatically debug missing assumptions. It begins by generating multiple formal counter-examples for the error. Next, a function is extracted from these counter-examples that encodes the input combinations that cause the assertion to fail. This function is later used to generate a list of fixed cycle assumptions that prevent failures similar to the generated counter-examples. These filtered assumptions can then be used as hints for the actual missing assumption. Further, if a missing assumption is not the cause of the failure, the method offers the additional benefit that the counter-examples it generates can be utilized to debug the RTL and/or the assertion. An extensive set of experimental results on OpenCores designs and assertions show that the number of generated assumptions can be reduced by an average of 38% using ten counter-examples, while an average of 28 assumptions is returned to the user.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".