The efficiency of a Pull Request (PR) process hinges on how quickly maintainers and contributors respond to each other. Knowing how long this might take can improve interactions and manage expectations.
Our new study introduces a machine-learning method to predict these response times by analyzing data from 20 popular open-source GitHub projects. We examine various features of the projects and PRs, and identified key factors that influence response times.
- PRs submitted earlier in the week, with a moderate number of commits and clear descriptions, tend to get quicker responses.
- Contributors who are more engaged and have a good track record also tend to respond faster.
- We also highlight how understanding and predicting response times can enhance the PR review process.
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Interested? You can find a pre-print of our paper here.