Leveraging LLM Enhanced Commit Messages to Improve Machine Learning Based Test Case Prioritization
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 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.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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