Leveraging LLM Enhanced Commit Messages to Improve Machine Learning Based Test Case Prioritization
Bibliographic record
Abstract
In the rapidly evolving landscape of software development, software testing is critical for maintaining code quality and reducing defects. Effective test case prioritization employs techniques to identify defects early and ensure software quality. New avenues of research have explored using machine learning (ML) to automate the process, most current applications leverage a machine learning model using numerical features to prioritize the test cases. This study investigates the enhancement of this process by incorporating text-based features derived from git commit messages, which often include valuable information about code changes. Given that commit messages are often poorly written and inconsistent, we employ a large language model (LLM) to rewrite these messages based on code diffs, with the aim of improving the quality of their format and the information they contain. We then assess whether these refined commit messages, as an additional feature, contribute to better performance of the test case prioritization model. Our preliminary results indicate that the inclusion of LLM-enhanced commit messages leads to a noticeable improvement in prioritization effectiveness, suggesting a promising avenue for integrating natural language processing techniques in software testing workflows.
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".