Relationship Evaluation for Developer Recommendation in Open Source Communities

In the process of open source software development, teamwork has become a mainstream trend. However, developers generally tend to communicate with acquaintances, and it is difficult to find high-quality unfamiliar developers, which brings a series of negative effects. To some extent, developers’ development efficiency and enthusiasm are negatively affected, and open source projects are easy to fall into the “homogenization trap”, and even die early. At the same time, the open source community faces the risk of isolation and rigidity. Therefore, developer recommendation is an important task for improving the efficiency of developers, promoting the rapid iteration of technology, and continuing to inject vitality into the open source community. To this end, we design an LLM-and-Edge enhanced HGT model (LEHGT), and investigate the developer recommendation task on the datasets constructed from the GitHub community. The core idea is to evaluate the relationships between developers and repositories in text and use the evaluation feature to enhance the HGT-based recommender. Experimental results show that the proposed model performs significantly better than comparative methods.