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Even though modern Integrated Development Environments (IDEs) support many refactorings, studies suggest that automated refactorings are used infrequently, and few developers use anything beyond Rename and Extract refactorings. Little is known about why automated refactorings are seldom used. We present a list of challenging questions whose answers are crucial for understanding the usability issues of refactoring tools. This paper argues that the existing data sources-Eclipse UDC, Eclipse refactoring histories, version control histories, etc.-are inadequate for answering these questions. Finally, we introduce our tools to collect richer usage data that will enable us to answer some of the open research questions about the usability of refactoring tools. Findings from our data will foster the design of the next generation of refactoring tools.

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Abstract. Managing friendship relationships in social media is challenging due to the growing number of people in online social networks (OSNs). To deal with this challenge, OSNs' users may rely on manually grouping friends with personally meaningful labels. However, manual grouping can become burdensome when users have to create multiple groups for various purposes such as privacy control, selective sharing, and filtering of content. More recently, recommendation-based grouping tools such as Facebook smart lists have been proposed to address this concern. In these tools, users must verify every single friend suggestion. This can hinder users' adoption when creating large content sharing groups. In this paper, we proposed an automated friend grouping tool that applies three clustering algorithms on a Facebook friendship network to create groups of friends. Our goal was to uncover which algorithms were better suited for social network groupings and how these algorithms could be integrated into a grouping interface. In a series of semi-structured interviews, we asked people to evaluate and modify the groupings created by each algorithm in our interface. We observed an overwhelming consensus among the participants in preferring this automated grouping approach to existing recommendation-based techniques such as Facebook smart lists. We also discovered that the automation created a significant bias in the final modified groups. Finally, we found that existing group scoring metrics do not translate well to OSN groupings-new metrics are needed. Based on these findings, we conclude with several design recommendations to improve automated friend grouping approaches in OSNs.

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