Mining Historical Test Failures to Dynamically Batch Tests to Save CI Resources
Bibliographic record
Abstract
Testing is a costly, time-consuming, and challenging part of modern software development. During continuous integration, after submitting each change, it is tested automatically to ensure that it does not break the system's functionality. A common approach to reducing the number of test case executions is to batch changes together for testing. For example, given four changes to test, if we group them in a batch and they pass we use one execution to test all four changes. However, if they fail, additional executions are required to find the culprit change that is responsible for the failure. We evaluate five batch culprit finding approaches: Dorfman, double pool testing, BatchBisect, BatchStop4, and our novel BatchDivide4. All prior works on batching use a constant batch size. In this work, we propose a dynamic batch size technique based on the weighted historical failure rate of the project. We simulate each of the batching strategies across 12 large projects on Travis with varying failures rate. We find that dynamic batching coupled with BatchDivide4 outperforms the other approaches. Compared to TestAll, this approach decreases the number of executions by 47.49% on average across the Travis projects. It outperforms the current state-of-the-art Batch4 by 5.17 percentage points. Our historical weighting approach leads us to a metric that describes the number of consecutive build failures. We find that the correlation between batch savings and FailureSpread is r = -0.97 with a p << 0.0001. This metric easily allows developers to determine the potential of batching on their project. We also contribute a theoretical limit for the savings that can be achieved by batch testing. We show that using dynamic batching, we achieve an across project average of 58.91% of the theoretical limit. Although batching is highly effective, there is still substantial room for improving batching relative to the theoretical batch savings limit. We make our scripts and data available for replication [1].
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".