The success of a Pull Request (PR) depends on the responsiveness of the maintainers and the contributor during the review process. Being aware of the expected waiting times can lead to better interactions and managed expectations for both the maintainers and the contributor. In this paper, we propose a machine-learning approach to predict the first response latency of the maintainers following the submission of a PR, and the first response latency of the contributor after receiving the first response from the maintainers. We curate a dataset of 20 large and popular open-source projects on GitHub and extract 21 features to characterize projects, contributors, PRs, and review processes. Using these features, we then evaluate seven types of classifiers to identify the best-performing models. We also perform permutation feature importance and SHAP analyses to understand the importance and impact of different features on the predicted response latencies. Our best-performing models achieve an average improvement of 33% in AUC-ROC and 58% in AUC-PR for maintainers, as well as 42% in AUC-ROC and 95% in AUC-PR for contributors compared to a no-skilled classifier across the projects. Our findings indicate that PRs submitted earlier in the week, containing an average or slightly above-average number of commits, and with concise descriptions are more likely to receive faster first responses from the maintainers. Similarly, PRs with a lower first response latency from maintainers, that received the first response of maintainers earlier in the week, and containing an average or slightly above-average number of commits tend to receive faster first responses from the contributors. Additionally, contributors with a higher acceptance rate and a history of timely responses in the project are likely to both obtain and provide faster first responses.

我们提出了一种基于机器学习的方法来预测在Pull Request提交后维护人员首次响应的延迟以及从维护人员获得首次响应后贡献者的首次响应延迟。我们使用20个大型热门开源项目的数据集进行特征提取，并使用七种分类器评估最佳模型。通过我们的研究，我们发现在一周的早期提交的PR，包含平均或略高于平均的提交数量以及简洁的描述更容易获得维护人员更快的首次响应。同样地，从维护人员那获得首次响应早期在一周内且包含平均或略高于平均的提交数量的PR更容易获得贡献者更快的首次响应。此外，在项目中具有较高接受率和及时响应历史的贡献者很可能能够更快地获得和提供首次响应。

预测维护者和贡献者在拉取请求中的首次响应延迟