PrioTestCI: Efficient Test Case Prioritization in GitHub Workflows for CI Optimization
Bibliographic record
Abstract
Continuous Integration (CI) is a widely adopted practice in software development to automatically verify code changes across diverse environments. However, executing the full test suite on every pull request update can lead to redundant runs, slower feedback loops, and inefficient utilization of CI resources. To address this issue, we introduce PrioTestCI, a prioritization technique within GitHub Actions that focuses on re-executing test cases that have previously failed. If these prioritized tests succeed, the remaining tests proceed; otherwise, the workflow terminates early, saving computation resources and providing early feedback to developers. PrioTestCI utilizes commit-to-commit test result tracking to inform future test runs, thereby reducing unnecessary repetition and accelerating validation cycles. We evaluated our technique on the Pytest project, a real-world open-source project with an extensive test matrix. PrioTestCI resulted in a CI runtime reduction of 1h57m39s compared to the normal workflow, with individual configuration improvements ranging from 63.75% to 91.94% (81.55% on average). Demo video: https://youtu.be/_3CF9LJdv0I?si=XyE_8mBnDxk1lMnD Repository: https://github.com/ShubhamDesai/CI-Optimization
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".