CsdRec: Accuracy and Diversity-awared Team Recommendation for Collaborative Software Development

Collaborative Software Development (CSD) as an effective software development paradigm has achieved rapid development. In CSD, one critical and challenging problem is to find a group of compatible and diverse developers who can complete the assigned task as quickly as possible. To solve this problem, most previous approaches focus on measuring the relevance of tasks and developers based on their historical interactions. However, such approaches usually suffer from data sparsity and cold start problem which may reduce the recommendation accuracy. Moreover, previous approaches often ignored the diversity of team members which may reduce the overall collaboration efficiency. In this paper, we propose an attentive text-enhanced graph embedding learning framework, termed as CsdRec. We first incorporate text embedding into one-hop interaction learning and two-hop graph learning to learn the relevance between tasks and team members. Then, we explicitly model the team members' diversity and incorporate it with relevance. Finally, to evaluate the performance of CsdRec, we conduct extensive experiments on two real-world datasets. The experimental results show that CsdRec outperforms other state-of-the-art approaches significantly.