Bibliographic record
Abstract
Unit testing plays an essential role in software development and maintenance, especially in Test-Driven Development. Conventional unit tests, which have no input parameters, often exercise similar scenarios with small variations to achieve acceptable coverage, which often results in duplicated code in test suites. Test code duplication hinders comprehension of test cases and maintenance of test suites. Test refactoring is a potential tool for developers to use to control technical debt arising due to test cloning. \nIn this thesis, we present a novel tool, JTestParametrizer, for automatically refactoring method-scope renamed clones in test suites. We propose three levels of refactoring to parameterize type, data, and behaviour differences in clone pairs. Our technique works at the Abstract Syntax Tree level by extracting a parameterized template utility method and \ninstantiating it with appropriate parameter values. \nWe applied our technique to 5 open-source Java benchmark projects and conducted an empirical study on our results. Our technique examined 14,431 test methods in our benchmark projects and identified 415 renamed clone pairs as effective candidates for refactoring. On average, 65% of the effective candidates (268 clone pairs) in our test suites are refactorable using our technique. All of the refactored test methods are compilable, and 94% of them pass when executed as tests. We believe that our proposed refactorings generally improve code conciseness, reduce the amount of duplication, and make test suites easier to maintain and extend.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".