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Method of Graphical User Interface Adaptation Using Reinforcement Learning and Automated Testing

Published: 08 March 2022 Publication History

Abstract

Abstract—Graphical user interface adaptation becomes an increasingly time-consuming and resource-intensive task due to modern programs complexity and a big variety of information output devices. In this paper we propose a method for adapting a graphical user interface based on a person's workflow using a specific implementation of the interface. This method makes it possible to adapt the interface to the peculiarities of the user's workflow through optimization in the navigation area between program windows.

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References

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Мезенков А. А., Шибанов С. В. Адаптация пользовательского интерфейса информационной системы к характеристикам пользователя // НиКа. 2012. №. URL: https://cyberleninka.ru/article/n/adaptatsiyapolzovatelskogointerfeysa-informatsionnoy-sistemy-kharakteristikam-polzovatelya (accessed date: 13.02.2019). J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
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Верлань Анатолий Фёдорович, Сопель Михаил Фёдорович, Фуртат Юрий Олегович. Об организации адаптивного пользовательского интерфейса в автоматизированных системах // Известия ЮФУ. Технические науки. 2014. №1 (150). URL: https://cyberleninka.ru/article/n/ob-organizatsiiadaptivnogopolzovatelskogo-interfeysa-v-avtomatizirovannyhsistemah (accessed date: 13.02.2019).
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Pierre A. Akiki, Arosha K. Bandara, and Yijun Yu. 2014. Adaptive model-driven user interface development systems. // ACM Comput. Surv. 47, 1, Article 9 (April 2014), 33 pages.
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NLPIR '21: Proceedings of the 2021 5th International Conference on Natural Language Processing and Information Retrieval
December 2021
175 pages
ISBN:9781450387354
DOI:10.1145/3508230
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 08 March 2022

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Author Tags

  1. Graphical user interface
  2. abstract user
  3. adaptation
  4. machine learning
  5. user workflow

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