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Automated metrics for user interface design and evaluation

A L Sears1

  • 1Computer Science Department, University of Maryland, College Park 20742.

International Journal of Bio-Medical Computing
|January 1, 1994
PubMed
Summary
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Automated metrics can evaluate user interfaces early in development, identifying issues before user testing. This approach saves time and money, leading to improved interfaces without extra costs.

Area of Science:

  • Human-Computer Interaction
  • Software Engineering
  • Usability Engineering

Background:

  • Evaluating user interfaces (UI) during development is crucial for creating effective and user-friendly products.
  • Traditional user testing is time-consuming and expensive, often occurring late in the development cycle.
  • There is a need for methods to assess UI quality early and efficiently.

Purpose of the Study:

  • To introduce automated metrics for evaluating user interfaces.
  • To assess the potential of automated metrics in predicting user preferences and performance.
  • To guide future research directions in automated UI evaluation.

Main Methods:

  • Development and introduction of both task-independent and task-sensitive automated metrics.

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  • Analysis of the predictive capabilities of these metrics for user preferences and performance.
  • Discussion of research avenues for refining and applying these metrics.

    • Main Results:

    • Automated metrics can facilitate early-stage UI evaluation, identifying potential problems before user testing.
    • Task-independent metrics show promise for predicting user preferences and, to some extent, performance.
    • Task-sensitive metrics are expected to be useful for predicting both user preferences and performance.

    Conclusions:

    • Automated metrics offer a cost-effective way to improve UI design by enabling early detection and correction of issues.
    • Both task-independent and task-sensitive automated metrics hold significant potential for enhancing UI development processes.
    • Further research is warranted to fully leverage the capabilities of automated metrics in predicting user experience.