一句话：提出自动开放信息抽取方法，发现并总结 Stack Overflow 中答案的争议讨论，并借助官方 API 文档辅助理解。
开发者常从 Stack Overflow 等社区问答网站寻找编程问题解决方案，但众包答案可能错误、次优或过时，而 accepted answer、投票等社区质量指标并不能可靠识别这些问题；其他用户的批评又不易在阅读帖子时被发现。本文将受批评的答案及其批评帖视为争议讨论，设计自动开放信息抽取方法，系统性地发现和总结 Stack Overflow 中的争议，并利用官方 API 文档辅助理解争议内容。作者在数百万个 Java/Android 标签的 Stack Overflow 问答上应用该方法，发现大规模争议讨论。人工评估表明抽取的争议信息准确率较高；18 名开发者参与的用户研究显示，生成的争议摘要能帮助开发者避开有争议的答案，为编程问题选择更合适的解决方案。
Developers often look for solutions to programming problems in community Q&A sites like Stack Overflow. Due to the crowdsourcing nature of these Q&A sites, many user-provided answers are wrong, less optimal or out-of-date. Relying on community-curated 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 Q&A 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 java/android-tagged 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.
DOI 原文 ·
@inproceedings{paperbot5242,
title = {Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites},
author = {Xiaoxue Ren and Zhenchang Xing and Xin Xia and Guoqiang Li and Jianling Sun},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00024}
}