The Importance of Accounting for Execution Failures when Predicting Test Flakiness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flaky tests are tests that pass and fail on different executions of the same version of a program under test. They waste valuable developer time by making developers investigate false alerts (flaky test failures). To deal with this issue, many prediction methods have been proposed. However, the utility of these methods remains unclear since they are typically evaluated based on single-release data, ignoring that in many cases tests that fail flakily in one release also correctly fail (indicating the presence of bugs) in some other, meaning that it is possible for subsequent correctly-failing cases to pass unnoticed. In this paper, we show that this situation is prevalent and can raise significant concerns for both researchers and practitioners. In particular, we show that flaky tests, tests that exhibit flaky behaviour at some point in time, have a strong fault-revealing capability, i.e., they reveal more than 1/3 of all encountered regression faults. We also show that 76.2%, of all test executions that reveal faults in the codebase under test are made by tests that are classified as flaky by existing prediction methods. Overall, our findings motivate the need for future research to focus on predicting flaky test executions instead of flaky tests.
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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 | 0.001 |
| 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 | 0.001 | 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