Test Redundancy Measurement Based on Coverage Information: Evaluations and Lessons Learned
Bibliographic record
Abstract
Measurement and detection of redundancy in test suites attempt to achieve test minimization which in turn can help reduce test maintenance costs, and to also ensure the integrity of test cases. Test suite reduction based on coverage information has been discussed in many previous works. However, the applications of such techniques on real test suites and realistic measurements of redundancy have not yet been experimented thoroughly. To address such a need, we formulate in this paper two experimental metrics for coverage-based measurement of test redundancy in the context of JUnit test suites. We then evaluate the approach by measuring the redundancy of four real Java projects. The automated measures are compared with manual redundancy decisions (performed through an inspection by a tester). The results and lessons learned are interesting and somewhat surprising in that besides they show usefulness of coverage information, they present a set of shortcomings (in terms of precision) for the simplistic coverage-based redundancy measurement approach as discussed in the literature. The root-cause analysis of our observations identify several key lessons learned which should help the testing researchers and practitioners in devising better techniques for more precise measurement of test redundancy.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".