一句话：用图神经网络从 GitHub 社交网络学习开发者技术专长嵌入，并在四类推荐任务上验证有效性。
开源社区中的许多任务（如挑选合适的维护者或评审者）都依赖对开发者技术专长的准确刻画，但 GitHub 并不显式展示开发者的技术专长，现有方法也难以完整捕捉其技能与知识的多面性和动态性。为此，作者提出一种基于图神经网络（GNN）的技术专长推导方法：构建融合社交关系与开发活动的 GitHub 社交网络，并在此基础上为开发者的技术专长学习低维嵌入表示。作者在四个 GitHub 社交关系推荐任务上对模型进行验证，结果表明该方法在预测开发者对仓库的技术偏好以及开发者之间的偏好方面均表现良好。
Developers' technical expertise is crucial for various tasks within open-source communities, such as identifying suitable maintainers or reviewers. However, GitHub, the world's largest open-source code hosting platform, does not explicitly display developers' technical expertise. Existing methods fail to fully capture the multifaceted and dynamic nature of their skills and knowledge. To address this problem, we propose a novel approach to derive developers' technical expertise using graph neural networks (GNN). We construct a GitHub social network to integrate social and development activities and employ a GNN model to learn low-dimensional embedding for developers’ technical expertise. We verify the effectiveness of our model on four GitHub social relationship recommendation tasks. The results demonstrate that our approach performs well in predicting technical preference for repositories and developers.CCS CONCEPTS•Software and its engineering → Search-based software engineering
@inproceedings{SunWZXWZ0024,
title = {Automatically Deriving Developers’ Technical Expertise from the GitHub Social Network},
author = {Yanchun Sun and Jiawei Wu and Xiaohan Zhao and Haizhou Xu and Sihan Wang and Jiaqi Zhang and Ye Zhu and Gang Huang},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695329}
}