Question Answering (CQA) websites have become valu- able repositories which host a massive volume of human knowl- edge. To maximize the utility of such knowledge, it is essential to evaluate the quality of an existing question or answer, especially soon after it is posted on the CQA website. In this paper, we study the problem of inferring the quality of ques- tions and answers through a case study of a software CQA (Stack Overflow). Our key finding is that the quality of an answer is strongly positively correlated with that of its question. A rmed with this observation, we propose a family of algorithms to jointly pre- dict the quality of questions and answers, for both quantifying nu- merical quality scores and differentiating the high-quali ty ques- tions answers from those of low quality. We conduct extensive experimental evaluations to demonstrate the effectiveness and ef- ficiency of our methods.
Question and Answer (Q&A) websites, such as Stack Overflow, use social media to facilitate knowledge exchange between programmers and fill archives with millions of entries that contribute to the body of knowledge in software development. Understanding the role of Q&A websites in the documentation landscape will enable us to make recommendations on how individuals and companies can leverage this knowledge effectively. In this paper, we analyze data from Stack Overflow to categorize the kinds of questions that are asked, and to explore which questions are answered well and which ones remain unanswered. Our preliminary findings indicate that Q&A websites are particularly effective at code reviews and conceptual questions. We pose research questions and suggest future work to explore the motivations of programmers that contribute to Q&A websites, and to understand the implications of turning Q&A exchanges into technical mini-blogs through the editing of questions and answers.  This paper analyzes a Question & Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Over 92 of Stack Overflow questions about expert topics are answered - in a median time of 11 minutes. Using a mixed methods approach that combines statistical data analysis with user interviews, we seek to understand this success. We argue that it is not primarily due to an a priori superior technical design, but also to the high visibility and daily involvement of the design team within the community they serve. This model of continued community leadership presents challenges to both CSCW systems research as well as to attempts to apply the Stack Overflow model to other specialized knowledge domains. Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.