Automatically Deriving Developers' Technical Expertise from the GitHub Social Network

Yanchun Sun

doi:10.1145/3746451

Automatically Deriving Developers' Technical Expertise from the GitHub Social Network

2026-04-01

0

PRE

AI

DOI：10.1145/3746451

Original request for help

Save

Abstract

En 中文

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.

Keywords:

GNN

deep learning

technical expertise

recommendation system

GitHub

Automatically Deriving Developers' Technical Expertise from the GitHub Social Network

Abstract

Journal

Organization