Profiling developer expertise across software communities with heterogeneous information network analysis

Knowing developer expertise is critical for achieving effective task allocation. However, it is of great challenge to accurately profile the expertise of developers over the Internet as their activities often disperse across different online communities. In this regard, the existing works either merely concern a single community, or simply sum up the expertise in individual communities. The former suffers from low accuracy due to incomplete data, while the latter impractically assumes that developer expertise is completely independent and irrelavant across communities. To overcome those limitations, we propose a new approach to profile developer expertise across software communities through heterogeneous information network (HIN) analysis. A HIN is first built by analyzing the developer activities in various communities, where nodes represent objects like developers and skills, and edges represent the relations among objects. Second, as random walk with restart (RWR) is known for its ability to capture the global structure of the whole network, we adopt RWR over the HIN to estimate the proximity of developer nodes and skill nodes, which essentially reflects developer expertise. Based on the data of 72,645 common users of GitHub and Stack Overflow, we conducted an empirical study and evaluated developer expertise using proposed approach. To evaluate the effect of our approach, we use the obtained expertise to estimate the competency of developers in answering the questions posted in Stack Overflow. The experimental results demonstrate the superiority of our approach over existing methods.