Mining Developer Behavior Across GitHub and StackOverflow
Yunxiang Xiong, Zhangyuan Meng, Beijun Shen, Wei Yin · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2017
Nowadays, software developers are increasingly involved in GitHub and StackOverflow, creating a lot of valuable data in the two communities.Researchers mine the information in these software communities to understand developer behaviors, while previous work mainly focuses on mining data within a single community.In this paper, we propose a novel approach to mining developer behaviors across GitHub and StackOverflow.This approach links the accounts from two communities using a CART decision tree, leveraging the features from usernames, user behaviors and writing styles.Then, it explores cross-site developer behaviors through T-graph analysis, LDA-based topics clustering and cross-site tagging.We conducted several experiments to evaluate this approach.The results show that the precision and F-Score of our identity linkage method are higher than previous methods in software communities.Especially, we discovered that (1) active issue committers are also active question askers; (2) for most developers, the topics of their contents in GitHub are similar to that of their questions and answers in StackOverflow;(3) developers' concerns in StackOverflow shift over the time of their current participating projects in GitHub; (4) developers' concerns in GitHub are more relevant to their answers than questions and comments in StackOverflow.