Size-Constrained Regression Test Case Selection Using Multicriteria Optimization
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
To ensure that a modified software system has not regressed, one approach is to rerun existing test cases. However, this is a potentially costly task. To mitigate the costs, the testing effort can be optimized by executing only a selected subset of the test cases that are believed to have a better chance of revealing faults. This paper proposes a novel approach for selecting and ordering a predetermined number of test cases from an existing test suite. Our approach forms an Integer Linear Programming problem using two different coverage-based criteria, and uses constraint relaxation to find many close-to-optimal solution points. These points are then combined to obtain a final solution using a voting mechanism. The selected subset of test cases is then prioritized using a greedy algorithm that maximizes minimum coverage in an iterative manner. The proposed approach has been empirically evaluated and the results show significant improvements over existing approaches for some cases and comparable results for the rest. Moreover, our approach provides more consistency compared to existing approaches.
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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 | 0.001 |
| 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