An End-to-End Test Case Prioritization Framework using Optimized Machine Learning Models
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
Regression testing in software development is challenging due to the large number of test cases and continuous integration (CI) practices. Recently, test case prioritization (TCP) using machine learning (ML) has been shown to efficiently execute regression tests. This study introduces an automated, endto-end, self-contained ML-based framework, TCP-Tune, tailored exclusively for TCP. The framework utilizes open-source version control system data to combine code-change-related features with test execution results. This integration allows the automated optimization of hyperparameters across different ML models to improve the TCP. The framework also effectively visualizes and utilizes multiple evaluation metrics to evaluate the performance of the model over several builds. Unlike existing implementations, which rely on various frameworks, TCP-Tune enables the effortless incorporation of features from multiple sources and fine-tuned models, thereby providing optimum test prioritization in the ever-changing field of software development. Our approach has helped to provide efficient TCP through experimental assessments of a real-life, large-scale CI system.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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