Safe regression test suite optimization: A review
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
Systems are frequently regression tested for frequently occurring changes due to corrective, preventive, adaptive or perfective actions. Regression testing is used to prevent the undesired effect of these changes on the previously tested version. Due to these changes, new test cases become part of the test suite making it huge and inefficient for `retest all' strategy. The ultimate solution of this problem is optimization or reduction of the test suite. Computational intelligence (CI) based approaches like evolutionary computation, fuzzy logic, neural networks and swarm optimization has been used for test suite reduction. Optimization approaches reduce the test suite by compromising its safety. Ideally optimization of test suite must guarantee safe reduction. In this work, we have optimized the test suite using some CI based approaches and then analyzed the test suite for `safe reduction'. Safe reduction can be gauged using control flow graphs. Test cases of optimal solutions were traversed on these graphs. We found that these solutions partially cover control flow graph. This showed that optimal solutions returned by CI based approaches except fuzzy logic are not safe and will be inadequate for regression testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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