Optimizing Test Case Reduction in CI/CD Pipelines Using Integer Linear Programming
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
In modern software development, Continuous Integration and Continuous Deployment (CI/CD) pipelines rely on the frequent execution of large test suites to detect regressions and maintain system reliability. However, executing the full suite in every build cycle imposes significant time and resource costs. While recent advancements in machine learning have enabled the prediction of test case failures, allowing for selective test execution, they may still lead to suboptimal coverage or high failure miss rates. This paper presents an optimization-based test case reduction approach that aims to minimize the number of wrongly eliminated test cases that would have failed in execution. By utilizing feature-engineered historical data from prior test executions, we compute a criticality score for each test case and use ILP to select an optimal subset to retain. This formulation minimizes the number of failed test cases that are wrongly excluded, while constraining test suite size to a desired execution range. We evaluate the method across large quantities of tests and demonstrate the practical viability of ILP-based test case reduction optimization for CI/CD pipelines.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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