Адаптация модели GOMS для мобильных приложений

A. V. Uzhentseva; E. Yu. Merzlyakova

doi:10.47813/2782-2818-2024-4-2-0230-0241

Authors

A. V. Uzhentseva https://orcid.org/0009-0007-8615-3647
E. Yu. Merzlyakova https://orcid.org/0000-0001-6847-5588

DOI:

https://doi.org/10.47813/2782-2818-2024-4-2-0230-0241

Keywords:

GOMS, mobile applications, adaptation, testing, methods, goals, operators, selection rules

Abstract

The article highlights the trend towards universal involvement of modern humanity in the digital world at the same time as a high level of concern for their own physical and mental comfort. This problem, including today, forces market participants not only to sell their products and services and not even start websites, but to switch to the development of mobile applications that will always be at the consumer's fingertips. Moreover, to ensure competitiveness, the interface of these applications should be user-friendly and understandable. In connection with this need, the problem of the lack of mobile device testing methods in general and quantitative methods in particular becomes obvious. This problem is the main focus of the article. Examples of testing methods for separate mobile interfaces and combined methods developed initially for desktop devices and subsequently adapted for mobile devices are given. The definition of one of the desktop quantitative methods – GOMS analysis – is given and its elements are listed. The article also refers to a number of other GOMS adaptation methods that were proposed earlier by other authors. The conclusion is made about the inconsistency of these methods with modern realities. A variant of the GOMS method for mobile applications is proposed, which includes all measurable operators that can be used when interacting with mobile applications through both touch and push-button devices. The results of the experiment are demonstrated, demonstrating the execution time of operators, which can be used as a guideline for conducting tests and further research on this topic.

Author Biographies

A. V. Uzhentseva

Anastasiya V. Uzhentseva, master student, Siberian State University of Telecommunications and Information Science (SibSUTIS), 86, Kirov St, Novosibirsk, 630102, Russian Federation

E. Yu. Merzlyakova

Ekaterina Y. Merzlyakova PhD (Engineering), Docent; Docent of the Department of Applied Mathematics and Cybernetics, Siberian State University of Telecommunications and Information Science (SibSUTIS), 86, Kirov St, Novosibirsk, 630102, Russian Federation

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Al-Megren S. A Predictive Fingerstroke-Level Model for Smartwatch Interaction. Multimodal Technol. Interact. 2018; 2: 38. https://doi.org/10.3390/mti2030038 DOI: https://doi.org/10.3390/mti2030038

Li H., Liu Y., Liu J., Wang X., Li Y., Rau P.L.P. Extended KLM for Mobile Phone Interaction: A User Study Result. In Proceedings of the CHI ’10 Extended Abstracts on Human Factors in Computing Systems. Atlanta, GA, USA, 10–15 April; 2010: 3517-3522. https://doi.org/10.1145/1753846.1754011 DOI: https://doi.org/10.1145/1753846.1754011

Rice A.D., Lartigue J.W. Touch-level Model (TLM): Evolving KLM-GOMS for Touchscreen and Mobile Devices. In Proceedings of the 2014 ACM Southeast Regional Conference. Kennesaw, GA, USA, 28–29 March; 2014: 53. https://doi.org/10.1145/2638404.2638532 DOI: https://doi.org/10.1145/2638404.2638532

El Batran K., Dunlop M.D. Enhancing KLM (Keystroke-level Model) to Fit Touch Screen Mobile Devices. In Proceedings of the 16th International Conference on Human-computer Interaction with Mobile Devices, MobileHCI ’14, Toronto, ON, Canada, 23–26 September 2014; ACM: New York, NY, USA; 2014: 283-286. https://doi.org/10.1145/2628363.2628385 DOI: https://doi.org/10.1145/2628363.2628385

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