How disabled tests manifest in test maintainability challenges?
Bibliographic record
Abstract
Software testing is an essential software quality assurance practice. Testing helps expose faults earlier, allowing developers to repair the code and reduce future maintenance costs. However, repairing (i.e., making failing tests pass) may not always be done immediately. Bugs may require multiple rounds of repairs and even remain unfixed due to the difficulty of bug-fixing tasks. To help test maintenance, along with code comments, the majority of testing frameworks (e.g., JUnit and TestNG) have also introduced annotations such as @Ignore to disable failing tests temporarily. Although disabling tests may help alleviate maintenance difficulties, they may also introduce technical debt. With the faster release of applications in modern software development, disabling tests may become the salvation for many developers to meet project deliverables. In the end, disabled tests may become outdated and a source of technical debt, harming long-term maintenance. Despite its harmful implications, there is little empirical research evidence on the prevalence, evolution, and maintenance of disabling tests in practice. To fill this gap, we perform the first empirical study on test disabling practice. We develop a tool to mine 122K commits and detect 3,111 changes that disable tests from 15 open-source Java systems. Our main findings are: (1) Test disabling changes are 19% more common than regular test refactorings, such as renames and type changes. (2) Our life-cycle analysis shows that 41% of disabled tests are never brought back to evaluate software quality, and most disabled tests stay disabled for several years. (3)We unveil the motivations behind test disabling practice and the associated technical debt by manually studying evolutions of 349 unique disabled tests, achieving a 95% confidence level and a 5% confidence interval. Finally, we present some actionable implications for researchers and developers.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".