Accelerating Continuous Integration with Parallel Batch Testing
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
Continuous integration at scale is costly but essential to software development. Various test optimization techniques including test selection and prioritization aim to reduce the cost. Test batching is an effective alternative, but overlooked technique. This study evaluates parallelization's effect by adjusting machine count for test batching and introduces two novel approaches. We establish TestAll as a baseline to study the impact of parallelism and machine count on feedback time. We re-evaluate ConstantBatching and introduce DynamicBatching, which adapts batch size based on the remaining changes in the queue. We also propose TestCaseBatching, enabling new builds to join a batch before full test execution, thus speeding up continuous integration. Our evaluations utilize Ericsson's results and 276 million test outcomes from open-source Chrome, assessing feedback time, execution reduction, and providing access to Chrome project scripts and data. The results reveal a non-linear impact of test parallelization on feedback time, as each test delay compounds across the entire test queue. ConstantBatching, with a batch size of 4, utilizes up to 72% fewer machines to maintain the actual average feedback time and provides a constant execution reduction of up to 75%. Similarly, DynamicBatching maintains the actual average feedback time with up to 91% fewer machines and exhibits variable execution reduction of up to 99%. TestCaseBatching holds the line of the actual average feedback time with up to 81% fewer machines and demonstrates variable execution reduction of up to 67%. We recommend practitioners use DynamicBatching and TestCaseBatching to reduce the required testing machines efficiently. Analyzing historical data to find the threshold where adding more machines has minimal impact on feedback time is also crucial for resource-effective testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it