The best answer prediction by exploiting heterogeneous data on software development Q&A forum

Recently, Questions and Answers (Q&A) forum for software development (e.g. Stack Overflow) becomes popular. Identifying the best answer to a raised question is important for Q&A forum since the best answer which provides an excellent solution to the raised question may guide the developers to solve their problems in practice. However, the best answers are often not explicitly tagged by question owners. It would be time-consuming for other developers with the same question to check all candidate answers to find the appropriate one. In this paper, we propose a novel approach to predict the best answers to the questions raised on Stack Overflow by exploiting heterogeneous data sources on the forum. We extract different groups features from multiple data sources and combine them for final prediction via multi-view learning. Experimental results indicate that the proposed method is effective in identifying the best answers to questions raised on Stack Overflow.