Automatic Knowledge Sharing Across Communities: A Case Study on Android Issue Tracker and Stack Overflow

Collaborative development communities and knowledge sharing communities are highly correlated and mutually complementary. The knowledge sharing between these two types of open source communities can be very beneficial to both of them. However, it is a great challenge to automate this process. Current studies mainly focus on knowledge acquisition in one type of community, and few of them have tackle this problem efficiently. In this paper we take Android Issue Tracker and Stack Overflow as a case to study automatic knowledge sharing between them. We propose an automatic approach by integrating semantic similarity with temporal locality between Android issues and Stack Overdo posts to reveal the potential associations between them. Our approach exploits the rich semantics in fine-grained information of issues and posts for associations building, and explores the temporal correlations between issues and posts in-depth for associations ranking. Extensive experiments show that the precision of our approach reaches 54.82% for top 10 recommendations when recommending Stack Overflow posts to Android issues, and 66.83%in reverse. This is significantly higher than the state-of-the-art method.