Heuristic evaluation of user interfaces
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Refining the test phase of usability evaluation: how many subjects is enough?
Human Factors - Special issue: measurement in human factors
A mathematical model of the finding of usability problems
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
Usability Engineering
Metaphors of human thinking for usability inspection and design
ACM Transactions on Computer-Human Interaction (TOCHI)
Introducing item response theory for measuring usability inspection processes
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Comparison of techniques for matching of usability problem descriptions
Interacting with Computers
A pattern-based usability inspection method: first empirical performance measures and future issues
BCS-HCI '07 Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI...but not as we know it - Volume 2
Heterogeneity in the usability evaluation process
BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 1
Sample size in usability studies
Communications of the ACM
Reviewing and Extending the Five-User Assumption: A Grounded Procedure for Interaction Evaluation
ACM Transactions on Computer-Human Interaction (TOCHI)
Journal of Biomedical Informatics
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In cases where usability is a mission critical system quality it is becoming essential to know whether an evaluation study has identified the majority of existing defects. Previous work has shown that procedures for estimating the progress of evaluation studies have to account for variation in defect visibility; otherwise, harmful bias will happen. Here, a statistical model is introduced for estimating the number of not-yet-identified defects in a study. This approach also supports exact confidence intervals and can easily be adapted to estimate the required number of sessions. The method is evaluated and shown to, in most cases, provide accurate measures. A running example illustrates how practitioners may track the progress of their studies and make quantitatively informed decisions on when to finish.