Developers’ technical expertise is crucial for numerous tasks within open-source communities, such as identifying suitable developers and maintainers. Despite its significance, GitHub, the world’s largest open-source code hosting platform, does not explicitly display developers’ technical expertise. Existing methods fall short in capturing the multi-faceted and dynamic nature of developers’ skills and knowledge. To address this gap, we propose a novel approach that leverages graph neural networks (GNNs) to express developers’ technical expertise. Our method constructs a comprehensive GitHub social network that integrates various social and development activities. We then employ a GNN model to learn a low-dimensional representation vector for each developer, encapsulating their technical expertise across different dimensions. We assess the effectiveness of our model by comparing it against five baselines on three GitHub social relationship recommendation tasks, including SimDeveloper, ContributionRepo, and RepoMaintainer. Our proposed method outperforms these baselines, achieving improvements of 5.6–9.5% on Hit Ratio@10 and 3.4–11.1% on F1 score. These results demonstrate promising performance in predicting technical preferences for both repositories and developers. This research contributes to a more nuanced understanding of developer expertise in open-source communities and has potential implications for improving collaboration and project management on platforms like GitHub.

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.