Mining simulation metrics for failure triage in regression testing
Bibliographic record
Abstract
Design debugging poses a major bottleneck in modern VLSI CAD flows, consuming up to 60% of the verification cycle. The debug pain, however, worsens in regression verification flows at the pre-silicon stage where myriads of failures can be exposed. These failures need to be properly grouped and distributed among engineers for further analysis before the next regression run commences. This high-level and complex debug problem is referred to as failure triage and largely remains a manual task in the industry. In this paper, we propose an automated failure triage flow that mines information from both failing and passing tests during regression, and automatically performs a coarse-grain partitioning of the failures. The proposed framework combines formal tools and novel statistical metrics to quantify the likelihood of specific design components being the root-cause of the observed failures. These components are then used to represent failures as high-dimensional objects, which are grouped by applying data-mining clustering algorithms. Finally, the generated failure clusters are automatically prioritized and passed to the best suited engineers for detailed analysis. Experimental results show that the proposed approach groups related failures together with 90% accuracy on the average, and efficiently prioritizes the responsible design errors for 86% of the exposed failures.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".