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

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12950))

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Abstract

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.

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Acknowledgements

This work is funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Ref. UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Polytechnic of Viseu for their support.

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Authors and Affiliations

  1. Polytechnic of Viseu, Viseu, Portugal

    Daniel Cunha, Rui P. Duarte & Carlos A. Cunha

  2. CISeD – Research Centre in Digital Services, Viseu, Portugal

    Rui P. Duarte & Carlos A. Cunha

Authors
  1. Daniel Cunha
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  2. Rui P. Duarte
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  3. Carlos A. Cunha
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Corresponding author

Correspondence to Rui P. Duarte .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

A List of Sources Used for Tests

A List of Sources Used for Tests

Fourteen web applications were used to carry out the tests specified in Sect. 3.3, and are identified in Table 4: (a) mouse interactions only based on navigation; (b) keyboard interactions between fields using the Tab key; or (c) combination of mouse and keyboard interactions. Column (d) identifies web applications used for pattern fitting tests, as presented in Fig. 7.

Table 4. Websites used in tests and corresponding contexts of interaction
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Cunha, D., Duarte, R.P., Cunha, C.A. (2021). KLM-GOMS Detection of Interaction Patterns Through the Execution of Unplanned Tasks. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-86960-1_15

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