Volume 11 • Issue 1 • PP: 29–40 • 2026
Measuring Visibility and Usability Features in Mobile Application Interface Design
Abstract
Mobile application usability is often discussed after deployment through user reviews or task testing, but many visible design problems can be measured earlier from the interface itself. This paper presents a feature-based framework for quantifying mobile interface visibility, usability, and accessibility risk from screen-level design properties. The study defines a Mobile Interface Visibility–Usability Quality score using observable measures such as primary-action salience, visual density, tap-target adequacy, label completeness, contrast proxy, navigation depth, whitespace, and clutter. The analysis uses a structured extract following public Rico and UICrit-style mobile UI data, where screenshots, hierarchy information, and designer critique concepts support data-driven assessment. The results show that usability quality is not determined by a single visual property. Screens with strong contrast may still be difficult to use if feature discoverability is weak, and screens with many functions may remain usable when hierarchy and labels are clear. The paper contributes a measurement protocol, design risk taxonomy, empirical score analysis, and practical remediation loop for mobile app teams seeking objective evidence before user-facing release.
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References
[1] B. Deka, Z. Huang, C. Franzen, J. Hibschman, D. Afergan, Y. Li, J. Nichols, and R. Kumar, “Rico: A mobile app dataset for building data-driven design applications,” in Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, 2017, pp. 845–854.
[2] P. Duan, C.-Y. Chen, G. Li, B. Hartmann, and Y. Li, “UICrit: Enhancing automated design evaluation with a UI critique dataset,” in Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, 2024, article 114, pp. 1–16.
[3] J.Wu, Y.-H. Peng, X. Y. Li, A. Swearngin, J. P. Bigham, and J. Nichols, “UIClip: A data-driven model for assessing user interface design,” in Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology, 2024, article 131, pp. 1–16.
[4] L. A. M. Zaina, R. P. M. Fortes, V. Casadei, L. S. Nozaki, and D. M. B. Paiva, “Preventing accessibility barriers: Guidelines for using user interface design patterns in mobile applications,” Journal of Systems and Software, vol. 186, article 111213, 2022.
[5] P.Weichbroth, “Factors influencing the perceived usability of mobile applications,” arXiv:2502.11069, 2025.
[6] M. Gomez-Hernandez, X. Ferre, C. Moral, and E. Villalba-Mora, “Design guidelines of mobile apps for older adults: Systematic review and thematic analysis,” JMIR mHealth and uHealth, vol. 11, article e43186, 2023.
[7] M. Kristi´c, I. Zakarija, and Ž. Car, “Machine learning for adaptive accessible user interfaces: Overview and applications,” Applied Sciences, vol. 15, no. 23, article 12538, 2025.
[8] M. Gu, L. Pei, S. Zhou, M. Shen, Y. Wu, Z. Gao, Z. Wang, S. Shan, W. Jiang, Y. Li, and J. Bu, “Towards an inclusive mobile web: A dataset and framework for focusability in UI accessibility,” in Proceedings of the ACM Web Conference 2025, 2025, pp. 1–12.
[9] S. Feng, S. Ma, H.Wang, D. Kong, and C. Chen, “MUD: Towards a large-scale and noise-filtered UI dataset for modern style UI modeling,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024, article 523, pp. 1–14.
[10] G. Lu, S. Qu, and Y. Chen, “Understanding user experience for mobile applications: A systematic literature review,” Discover Applied Sciences, vol. 7, article 587, 2025.
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