KLM-GOMS Detection of Interaction Patterns Through the Execution of Unplanned Tasks

• Published in: Computational Science and Its Applications - ICCSA 2021 Vol. 12950; pp. 203 - 219
• Main Authors: Cunha, Daniel, Duarte, Rui P., Cunha, Carlos A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Human-centered computing; Interaction design; Keystroke level model; Software testing; User interface design
• ISBN: 9783030869595; 3030869598
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-86960-1_15

The availability of software applications has contributed to the increase in user demand, which has increased the complexity of these applications. This contributed to the adoption of automation mechanisms for the software testing process, in order to reduce coding errors and shorten the time needed to deploy a new version of the application to the user. Currently, automating the application testing process is a well-established reality and supported by many tools. However, the usability evaluation of an application requires solutions that allow to determine, in advance, the type of improvements that may be necessary in the application without the need for intensive user testing. This work deals with the automatic analysis of the impact on the user of changes in the design of an application, through the implementation of the Keystroke Level Model (KLM). Based on the execution of unplanned user interactions in a web interface, a KLM string is obtained and evaluated, providing a model that converts KLM operators and the execution time of each operator into information for designers. Moreover, performance indicators are obtained and interaction patterns are automatically defined.