Pre-Conference Talks by Bowen XU & Ferdian THUNG

The prevalence of questions and answers on domain- specific Q&A sites like Stack Overflow constitutes a core knowl- edge 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. The user study results demonstrate that answer summaries generated by our approach are relevant, useful and diverse; moreover, the two components are able to effectively retrieve relevant questions and select salient answer paragraphs for summarization.

As the carrier of Application Programming Interfaces (APIs) knowledge, API documentation plays a crucial role in how developers learn and use an API. It is also a valuable information resource for answering API-related questions, especially when developers cannot find reliable answers to their questions online/offline. However, finding answers to API-related questions from API documentation might not be easy because one may have to manually go through multiple pages before reaching the relevant page, and then read and understand the information inside the relevant page to figure out the answers. To deal with this challenge, we develop APIBot, a bot that can answer API questions given API documentation as an input. APIBot is built on top of SiriusQA, the QA system from Sirius, a state of the art intelligent personal assistant. To make SiriusQA work well under software engineering scenario, we make several modifications over SiriusQA by injecting domain specific knowledge. We evaluate APIBot on 92 API questions, answers of which are known to be present in Java 8 documentation. Our experiment shows that APIBot can achieve a Hit@5 score of 0.706.

Bowen XU is a PhD student at School of Information Systems, Singapore Management University advised by Associate Professor David Lo. He received his M.Eng. in College of Software Technology, Zhejiang University in 2014. His research interests are in software engineering area. He has been working on cross-language information retrieval techniques, empirical studies and text mining in software engineering.

Ferdian THUNG is a PhD candidate at School of Information Systems, Singapore Management University advised by Associate Professor David Lo. He received his B.Eng. in Informatics Engineering from School of Electrical Engineering and Informatics, Bandung Institute of Technology in 2011. From August 2015 to May 2016, he visited the Institute for Software Research in CMU. His research interests are in software engineering and data mining area. He has been working on automated prediction techniques, recommendation systems, and empirical studies in software engineering.