  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.
