Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites Developers often look for solutions to programming problems in community Qu0026A sites like Stack Overflow. Due to the crowdsourcing nature of these Qu0026A sites, many userprovided answers are wrong, less optimal or outofdate. Relying on communitycurated quality indicators (e.g., accepted answer, answer vote) cannot reliably identify these answer problems. Such problematic answers are often criticized by other users. However, these critiques are not readily discoverable when reading the posts. In this paper, we consider the answers being criticized and their critique posts as controversial discussions in community Qu0026A sites. To help developers notice such controversial discussions and make more informed choices of appropriate solutions, we design an automatic open information extraction approach for systematically discovering and summarizing the controversies in Stack Overflow and exploiting official API documentation to assist the understanding of the discovered controversies. We apply our approach to millions of javaandroidtagged Stack overflow questions and answers and discover a large scale of controversial discussions in Stack Overflow. Our manual evaluation confirms that the extracted controversy information is of high accuracy. A user study with 18 developers demonstrates the usefulness of our generated controversy summaries in helping developers avoid the controversial answers and choose more appropriate solutions to programming questions.

CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs Coding convention plays an important role in guaranteeing software quality. However, coding conventions are usually informally presented and inconvenient for programmers to use. In this paper, we present CocoQa, a system that answers programmeru0027s questions about coding conventions. CocoQa answers questions by querying a knowledge graph for coding conventions. It employs 1) a subgraph matching algorithm that parses the question into a SPARQL query, and 2) a machine comprehension algorithm that uses an endtoend neural network to detect answers from searched paragraphs. We have implemented CocoQa, and evaluated it on a coding convention QA dataset. The results show that CocoQa can answer questions about coding conventions precisely. In particular, CocoQa can achieve a precision of 82.92% and a recall of 91.10%. Repository: https:github.com14dtjCocoQa Video: https:youtu.beVQaXi1WydAU