A method to calculate necessary assignments in algorithmic test pattern generation
Why this work is in the frame
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Bibliographic record
Abstract
The authors present a novel test pattern generation algorithm which uses the concept of necessary assignments to reduce or eliminate backtracking in automatic test pattern generation. Necessary assignments are those which must be made in order to find a test pattern; without them the search is guaranteed to fail. The algorithm is based on the mathematical concept of images and inverse images of set functions. In order to take advantage of formal concepts developed for Boolean algebras, the algorithm uses a 16-valued algebra. It has been used to generate test patterns for all faults in a variety of benchmark circuits. Experimental results indicate that the algorithm is particularly efficient at redundancy identification, which is often a problem for conventional test pattern generation algorithms. The benefits of a 16-valued system are illustrated through examples of faults which are not properly handled by conventional 5- or 9-valued systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 |
| 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 it