A First Look at the Inheritance-Induced Redundant Test Execution
Bibliographic record
Abstract
Inheritance, a fundamental aspect of object-oriented design, has been leveraged to enhance code reuse and facilitate efficient software development. However, alongside its benefits, inheritance can introduce tight coupling and complex relationships between classes, posing challenges for software maintenance. Although there are many studies on inheritance in source code, there is limited study on using inheritance in test code. In this paper, we take the first step by studying inheritance in test code, with a focus on redundant test executions caused by inherited test cases. We empirically study the prevalence of test inheritance and its characteristics. We also propose a hybrid approach that combines static and dynamic analysis to identify and locate inheritance-induced redundant test cases. Our findings reveal that (1) inheritance is widely utilized in the test code, (2) inheritance-induced redundant test executions are prevalent, accounting for 13% of all execution test cases, (3) bypassing these redundancies can help reduce 14% of the test execution time, and finally, (4) our study highlights the need for careful refactoring decisions to minimize redundant test cases and identifies the need for further research on test code quality.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".