Główne pojęcia
The author argues that existing datasets for commit message generation have critical weaknesses, leading to the creation of CommitBench. By implementing best practices in dataset creation, CommitBench aims to address shortcomings and provide a benchmark for future research in commit message generation.
Streszczenie
Commit messages are crucial in software development, but writing them can be tedious. Existing datasets for commit message generation have limitations, prompting the creation of CommitBench. This new dataset aims to improve the quality of generated commit messages by addressing issues like privacy concerns and lack of diversity in existing datasets. CommitBench is designed to serve as a benchmark for evaluating models in commit message generation tasks.
Key points from the content:
- Writing informative commit messages is essential but often neglected.
- Existing datasets for commit message generation have various problems.
- CommitBench is introduced as a new dataset adopting best practices.
- The dataset aims to enhance the quality of generated commit messages and accelerate future research.
- Different filtering techniques are applied to ensure high-quality data in CommitBench.
Statystyki
Over 100 million commit messages are generated daily.
MCMD draws almost 2 million commits from only 500 repositories.
The CommitGen dataset has 53 million model parameters.
Cytaty
"Automating this task has the potential to save time while ensuring that messages are informative."
"We show that existing datasets exhibit various problems, such as the quality of the commit selection."