Avoiding the Familiar to Speed Up Test Case Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Delta Debugging is a longstanding approach to automated test case reduction. It divides an input into chunks and attempts to remove them to produce a smaller input. When a chunk is successfully removed, all chunks are revisited, as they may become removable from the smaller input. When no chunk can be removed, the chunks are subdivided and the process continues recursively. In the worst case, this revisiting behavior has an O(n^2) running time. We explore the possibility that good test case reduction can be achieved without revisiting, yielding an O(n) algorithm. We identify three independent conditions that can make this reasonable in practice and validate the hypothesis on a suite of user-reported and fuzzer-generated test cases. Results show that on a suite of large fuzzer-generated test cases for compilers, our O(n) approach yields reduced test cases with similar size, while decreasing the reduction time by 65% on average.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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