Maximal multiple fault coverage using single fault test sets
Bibliographic record
Abstract
In this paper, an analysis based on the sensitization structure behavior in the existance of multiple faults for the general class of combinational circuits is presented. The analysis is based on partitioning the set of primary inputs into three subsets S/sub e/ (excitation set), S/sub p/ (persistency set), and S/sub c/ (control set). The problem of augmenting a single fault test set to obtain a maximal multiple fault coverage is formulated as the one of maximizing the number of primary inputs in the S/sub c/ set (or minimizing the number of primary inputs in S/sub p/) It is shown that this analysis can be used in extending single fault test sets in order to achieve a maximal multiple fault coverage. We have presented a procedure for augmenting any single fault test set. An experiment has been carried out on the 74LS181 ALU using twelve single fault test sets. It is shown that different fault classes can be covered using the procedure presented in this paper. A 100% double fault coverage for all the single fault tests is achieved.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".