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<p>Log analysis is an effective user experience testing method for exploring subtle user interactions with commercial machines. The rapid iteration of commercial machines and their applications calls for a more cost-effective alternative to traditional in-the-wild approaches. However, lab-based log studies typically rely on customized testing programs, which do not represent real-world usage. Therefore, this study investigates the feasibility of log analysis using real-world applications in lab settings to explore user interaction. We present a case study exploring smartphone gesture interaction in more than 50 everyday operations across 10 real-world applications. We identified and analyzed detailed gesture parameters from log data on three common gesture sets, including tapping (single and double), zooming (pinching and spreading), and swiping (horizontal and vertical). Grounding in related works, we discuss observed behavior patterns, their potential causes, and implications for future design in commercial machine interfaces. We highlight the applicability of the presented approach by discussing its advantages, direction for improvements, and potential applications. We call for more studies to explore the norms and values of this approach.</p>

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Exploring User Interactions with Commercial Machines via Real-world Application Logs in the Lab

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Fangli Song,
  • Wei Wang,
  • Dasen Zhou,
  • Nick Bryan-Kinns,
  • Jun Zhang,
  • Qi Chen,
  • Le Du

摘要

Log analysis is an effective user experience testing method for exploring subtle user interactions with commercial machines. The rapid iteration of commercial machines and their applications calls for a more cost-effective alternative to traditional in-the-wild approaches. However, lab-based log studies typically rely on customized testing programs, which do not represent real-world usage. Therefore, this study investigates the feasibility of log analysis using real-world applications in lab settings to explore user interaction. We present a case study exploring smartphone gesture interaction in more than 50 everyday operations across 10 real-world applications. We identified and analyzed detailed gesture parameters from log data on three common gesture sets, including tapping (single and double), zooming (pinching and spreading), and swiping (horizontal and vertical). Grounding in related works, we discuss observed behavior patterns, their potential causes, and implications for future design in commercial machine interfaces. We highlight the applicability of the presented approach by discussing its advantages, direction for improvements, and potential applications. We call for more studies to explore the norms and values of this approach.