Non-Flaky and Nearly Optimal Time-Based Treatment of Asynchronous Wait Web Tests
Why this work is in the frame
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Bibliographic record
Abstract
Asynchronous waits are a common root cause of flaky tests and a major time-influential factor of Web application testing. We build a dataset of 49 reproducible asynchronous wait flaky tests and their fixes from 26 open source projects to study their characteristics in Web testing. Our study reveals that developers adjusted wait time to address asynchronous wait flakiness in about 63% of cases (31 out of 49), even when the underlying causes lie elsewhere. From this, we introduce TRaf , an automated time-based repair for asynchronous wait flakiness in Web applications. TRaf determines appropriate wait times for asynchronous calls in Web applications by analyzing code similarity and past change history. Its key insight is that efficient wait times can be inferred from the current or past codebase since developers tend to repeat similar mistakes. Our analysis shows that TRaf can statically suggest a shorter wait time to alleviate async wait flakiness immediately upon the detection, reducing test execution time by 11.1% compared to the timeout values initially chosen by developers. With optional dynamic tuning, TRaf can reduce the execution time by 16.8% in its initial refinement compared to developer-written patches and by 6.2% compared to the post-refinements of these original patches. Overall, we sent 16 pull requests from our dataset, each fixing one test, to the developers. So far, three have been accepted by the developers.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it