Exploring Test Suite Diversification and Code Coverage in Multi-Objective Test Case Selection
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 selection is a classic testing technique to choose a subset of existing test cases for execution, due to the limited budget and tight deadlines. While `code coverage' is the state of practice among test case selection heuristics, recent literature has shown that `test case diversity' is also a very promising approach. In this paper, we first compare these two heuristics for test case selection in several real-world case studies (Apache Ant, Derby, JBoss, NanoXML and Math). The results show that neither of the two techniques completely dominates the other, but they can potentially be complementary. Therefore, we next propose a novel approach that maximizes both code coverage and diversity among the selected test cases using NSGA-II multi- objective optimization, and the results show a significant improvement in fault detection rate. Specifically, sometimes this novel approach detects up to 16\%(Ant), 10\%(JBoss), and 14\% (Math) more faults compared to either of coverage or diversity-based approaches, when the testing budget is less than 20\% of the entire test suite execution cost.
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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 | 0.002 |
| 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 | 0.001 |
| 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