Bisecting commits and modeling commit risk during testing
Bibliographic record
Abstract
Software testing is one of the costliest stages in the software development life cycle. One approach to reducing the test execution cost is to group changes and test them as a batch (i.e. batch testing). However, when tests fail in a batch, commits in the batch need to be re-tested to identify the cause of the failure, i.e. the culprit commit. The re-testing is typically done through bisection (i.e. a binary search through the commits in a batch). Intuitively, the effectiveness of batch testing highly depends on the size of the batch. Larger batches require fewer initial test runs, but have a higher chance of a test failure that can lead to expensive test re-runs to find the culprit. We are unaware of research that investigates and simulates the impact of batch sizes on the cost of testing in industry. In this work, we first conduct empirical studies on the effectiveness of batch testing in three large-scale industrial software systems at Ericsson. Using 9 months of testing data, we simulate batch sizes from 1 to 20 and find the most cost-effective BatchSize for each project. Our results show that batch testing saves 72% of test executions compared to testing each commit individually. In a second simulation, we incorporate flaky tests that pass and fail on the same commit as they are a significant source of additional test executions on large projects. We model the degree of flakiness for each project and find that test flakiness reduces the cost savings to 42%. In a third simulation, we guide bisection to reduce the likelihood of batch-testing failures. We model the riskiness of each commit in a batch using a bug model and a test execution history model. The risky commits are tested individually, while the less risky commits are tested in a single larger batch. Culprit predictions with our approach reduce test executions up to 9% compared to Ericsson’s current bisection approach. The results have been adopted by developers at Ericsson and a tool to guide bisection is in the process of being added to Ericsson’s continuous integration pipeline.
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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 | 0.011 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".