Exemplar-based failure triage for regression design debugging
Bibliographic record
Abstract
Modern regression verification often exposes myriads of failures at the pre-silicon stage. Typically, these failures need to be properly grouped into bins, which then have to be distributed to engineers for detailed analysis. The above process is coined as failure triage, and is nowadays increasing in complexity, as the size of both design logic and verification environment continues to grow. However, it remains a predominantly manual process that can prolong the debug cycle and jeopardize time-sensitive design milestones. In this paper, we propose an exemplar-based data-mining formulation of Failure Triage that efficiently automates both failure grouping and bin distribution. The proposed framework maps failures as data points, applies an affinity-propagation (AP) clustering algorithm, and operates in both metric and non-metric spaces, offering complete flexibility and significant user control over the process. Experimental results show that the proposed approach groups related failures together with 87% accuracy on the average, and improves bin distribution accuracy by 21% over existing methods.
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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 | 0.001 | 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 | 0.000 |
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 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".