Understanding the Lifecycle of Flaky Tests and Identifying \nFlaky Failures
Bibliographic record
Abstract
Software testing is a critical aspect of ensuring the quality of software. Ideally, tests should \nproduce consistent results when being executed repeatedly on the same version of the software. \nHowever, certain tests may exhibit non-deterministic behavior, commonly known as ªflaky testsº. \nThese tests can provide ambiguous signals to developers and make the test results unreliable. \nDespite being a recognized phenomenon for decades, academic attention towards test flakiness \nhas only recently increased. The current dissertation aims to contribute to the advancement of \nresearch in two directions. First, we focus on predicting the lifetime of a flaky test, an issue that \nhas been left unaddressed in the flaky tests research area. Secondly, we question the efficiency of \nprevious studies in discerning flaky failures from legitimate failures, focusing on the Chromium \nbuild result as our dataset. \nIn our investigation of the historical patterns of flaky tests in Chrome, we identified that 40% of \nflaky tests remain unresolved, while 38% are typically addressed within the initial 15 days of introduction. \nSubsequently, we developed a predictive model focused on identifying tests with quicker \nresolutions. Our model demonstrated a precision of 73% and a Matthews Correlation Coefficient \n(MCC) approaching 0.39 in forecasting the lifespan class of flaky tests. \nFurthermore, we discovered that current vocabulary-based flaky test detection approaches misclassify \n78% of legitimate failures as flaky failures when applied to the Chromium dataset. The \nresults also revealed that the source code of tests is not enough indicator for predicting flaky failures, \nand other execution-related features must be contributed for better performance.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".