A Tester-Assisted Methodology for Test Redundancy Detection
Why this work is in the frame
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Bibliographic record
Abstract
Test redundancy detection reduces test maintenance costs and also ensures the integrity of test suites.One of the most widely used approaches for this purpose is based on coverage information.In a recent work, we have shown that although this information can be useful in detecting redundant tests, it may suffer from large number of false-positive errors, that is, a test case being identified as redundant while it is really not.In this paper, we propose a semiautomated methodology to derive a reduced test suite from a given test suite, while keeping the fault detection effectiveness unchanged.To evaluate the methodology, we apply the mutation analysis technique to measure the fault detection effectiveness of the reduced test suite of a real Java project.The results confirm that the proposed manual interactive inspection process leads to a reduced test suite with the same fault detection ability as the original test suite.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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