Automated debugging of missing input constraints in a formal verification environment
Bibliographic record
Abstract
Abstract—In the past decade, formal tools have increased functional verification efficiency by exhaustively searching for hard to find bugs. Often the counter-examples returned are not due to design bugs but due to missing constraints that are needed to model the surrounding environment. These types of false positives have become a great concern in the industry today. To address this issue, input constraints are typically added by the engineer to restrict the input space a formal tool is allowed to explore. These constraints are difficult to generate as they are usually implicit in the documentation or implementation of adjacent design blocks. As a consequence, this process reduces the efficiency of formal methodologies because missing input constraints must be determined before deep design bugs can actually be detected. In this work, we present an algorithm to automatically generate missing input constraints given a failing counter-example. The process begins by building a filtering function that models the failing behaviors from the counter-example. Next, using this function a list of fixed cycle properties are generated and filtered to return a set of candidate input constraints for use in debugging. Preliminary experimental results show that the generated properties provide a strong intuition as to what input constraints may be missing. I.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".