Complexity analysis: a quantitative approach to usability engineering

  • Authors:
  • Rick Sobiesiak;Tim O'Keefe

  • Affiliations:
  • IBM Canada Laboratory;IBM Rochester Laboratory

  • Venue:
  • Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
  • Year:
  • 2011

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Abstract

This paper describes complexity analysis, a quantitative approach to software usability engineering that has been successfully leveraged in several real-world projects. Complexity analysis is based on finding and quantifying impediments that get in the way of easily learning and using software. These impediments -- such as long sequences of manual steps, confusing user interfaces, and cryptic error messages -- are quantified by measures we call "complexity metrics". These metrics provide easily-understood usability comparisons between steps in a task, overall tasks, releases, and products. They are generated through rigorous, detailed rating scales associated with the following six aspects of software usability: context shifts, navigational guidance, input parameters, system feedback, error feedback, and new concepts. Although complexity analysis is a lighter-weight usability evaluation method than usability testing, empirical results show that complexity metrics are strongly correlated to usability testing time-on-task measures.