AnswerBot: Automated generation of answer summary to developers’ technical questions

• Main Authors: XU, Bowen, XING, Zhenchang, XIA, Xin, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: summary generation; question retrieval; Databases and Information Systems; Numerical Analysis and Scientific Computing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3924; https://ink.library.smu.edu.sg/context/sis_research/article/4926/viewcontent/AnswerBot_ase17.pdf
• Institution: Singapore Management University

The prevalence of questions and answers on domain-specific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details of the posts. We formulate our task as a query-focused multi-answer-posts summarization task for a given technical question. Our proposed approach AnswerBot contains three main steps : 1) relevant question retrieval, 2) useful answer paragraph selection, 3) diverse answer summary generation. To evaluate our approach, we build a repository of 228,817 Java questions and their corresponding answers from Stack Overflow. We conduct user studies with 100 randomly selected Java questions (not in the question repository) to evaluate the quality of the answer summaries generated by our approach and the effectiveness of its relevant question retrieval and answer paragraph selection components. Our evaluation shows that answer summaries generated by our approach are relevant, useful and diverse to developers’ technical questions, and its components can effectively retrieve relevant questions and select salient answer paragraphs for summarization.