Test Case Prioritization Using Lexicographical Ordering
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
Test case prioritization aims at ordering test cases to increase the rate of fault detection, which quantifies how fast faults are detected during the testing phase. A common approach for test case prioritization is to use the information of previously executed test cases, such as coverage information, resulting in an iterative (greedy) prioritization algorithm. Current research in this area validates the fact that using coverage information can improve the rate of fault detection in prioritization algorithms. The performance of such iterative prioritization schemes degrade as the number of ties encountered in prioritization steps increases. In this paper, using the notion of lexicographical ordering, we propose a new heuristic for breaking ties in coverage based techniques. Performance of the proposed technique in terms of the rate of fault detection is empirically evaluated using a wide range of programs. Results indicate that the proposed technique can resolve ties and in turn noticeably increases the rate of fault detection.
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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 | 0.001 |
| 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