ArguLens：使用论证模型剖析社区对可用性问题的意见
ArguLens: Anatomy of Community Opinions On Usability Issues Using Argumentation Models
摘要：
在开放源码软件中，可用性的设计往往受到社区成员在问题跟踪系统等平台上的讨论的影响。然而，由于评论的数量和多样性，消化问题讨论中包含的丰富信息可能是一项重大挑战。我们提出并评估了ArguLens，一个概念框架和自动化技术，利用论证模型来支持有效理解和巩固ITSs中的社区意见。通过内容分析，我们从一个大型的、活跃的OSS项目中剖析了高度讨论的可用性问题，并将其分解为论证组件和观点。然后，我们尝试了有监督的机器学习技术来自动提取论点。最后，通过与经验丰富的ITS用户的研究，我们表明，ArguLens提供的信息支持消化的可用性相关的意见，并促进了冗长的问题的审查。ArguLens为设计有价值的工具提供了方向，这些工具用于高级推理和关于可用性的有效讨论。
英文摘要：
In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
