Rechecking Recheck Requests in Continuous Integration: An Empirical Study of OpenStack
Bibliographic record
Abstract
Continuous Integration (CI) is a process for automatically checking patch sets for errors. CI periodically fails due to non-deterministic (a.k.a., "flaky") behaviour. Since a patch set may not be the cause of a flaky failure, developers can issue a "recheck" command to request retesting a patch set. Developers waste time considering whether or not to issue a recheck after a CI failure. Prior work also shows that rechecks are issued liberally, wasting up to 187.4 compute years when CI continues to fail. To save developer time and avoid wasteful rechecks, we fit and analyze statistical models that discriminate between successful and failing rechecks, i.e., those rechecks that will change a failing CI run into a successful one and those that will fail again. Through an empirical study of 314,947 recheck requests from OpenStack, we find that our model can differentiate successful and failed rechecks well, outperforming baseline approaches by 23.6 percentage points in terms of AUROC (0.736).Analysis of our model suggests that, in terms of explanatory power, past behaviour of jobs, bots, and users dominate static characteristics of patch sets. Applying our model to automatically request rechecks for those predicted to succeed would have saved roughly 247 years of elapsed developer time for OpenStack. Applying our model to skip recheck requests when they are predicted to fail would avoid 86.49% of wasted rechecks, saving roughly 262 years of compute time.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".