Mining of Interface Components and Metadata in Mobile Applications
Abstract
Introduction: Quality interfaces are essential for the success of mobile applications. Objective: This work proposes the integration of metadata and interface characteristics through the creation of two complementary datasets: Automated Insights Dataset (AID) and User Interface Depth Dataset (UID). Methodology: AID gathered metadata from 6,400 of the most downloaded free apps from Google Play. UID, derived from AID, manually mapped 7,540 interface components and 1,948 screenshots from 400 applications. Data analysis and machine learning techniques were applied to identify patterns and predict the presence of components from textual descriptions. Results: AID revealed market trends and recurring technical requirements. UID identified relevant usage patterns, component correlations, and variations according to categories, colors, and design practices. The model achieved an accuracy of over 80%. The findings demonstrate the potential of the datasets for analyzing design trends, personalizing user experience, and developing interface design support tools.
Keywords:
Dataset, Mobile Applications, User Interface, Machine Learning, Natural Language Processing
References
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Verma, J. P. (2012). Data analysis in management with SPSS software. Springer Science & Business Media.
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Yu, H., Lian, Y., Yang, S., Tian, L., e Zhao, X. (2016). Recommending features of mobile applications for developer. In International Conference on Advanced Data Mining and Applications, pages 361–373. Springer.
Adler, J. e Parmryd, I. (2010). Quantifying colocalization by correlation: the pearson correlation coefficient is superior to the mander’s overlap coefficient. Cytometry Part A, 77(8):733–742.
Bowers, A. J., Zhao, Y., e Ho, E. (2022). Towards hierarchical cluster analysis heatmaps as visual data analysis of entire student cohort longitudinal trajectories and outcomes from grade 9 through college. The High School Journal, 106(1):5–36.
Bunian, S., Li, K., Jemmali, C., Harteveld, C., Fu, Y., e Seif El-Nasr, M. S. (2021). Vins: Visual search for mobile user interface design. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–14.
Chen, X., Zou, Q., Fan, B., Zheng, Z., e Luo, X. (2019). Recommending software features for mobile applications based on user interface comparison. Requirements Engineering, 24(4):545–559.
Clifton, I. G. (2015). Android user interface design: Implementing material design for developers. Addison-Wesley Professional.
da Cruz Alves, N., Kreuch, L., e von Wangenheim, C. G. (2022). Analyzing structural similarity of user interface layouts of android apps using deep learning. In Anais do XXI Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, Porto Alegre, RS, Brasil. SBC.
de Souza Lima, A. L., Martins, O. P. H. R., von Wangenheim, C. G., von Wangenheim, A., Borgatto, A. F., e Hauck, J. C. R. (2022). Automated assessment of visual aesthetics of android user interfaces with deep learning. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems, IHC ’22, New York, NY, USA. Association for Computing Machinery.
Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., Nichols, J., e Kumar, R. (2017). 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, UIST ’17. ACM.
Fonseca, J. S. d. e Martins, G. d. A. (2016). Curso de estatística. Atlas, São Paulo, SP, 6ª edition.
Gorla, A., Tavecchia, I., Gross, F., e Zeller, A. (2014). Checking app behavior against app descriptions. In Proceedings of the 36th international conference on software engineering, pages 1025–1035.
Harty, J. e Müller, M. (2019). Better android apps using android vitals. In Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics, WAMA 2019, page 26–32, New York, NY, USA. Association for Computing Machinery.
Hecht, G. e Bergel, A. (2021). Quantifying the adoption of kotlin on android stores: Insight from the bytecode. In 2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft), pages 94–98.
Kabir, M. S. e Arefn, M. S. (2019). Google play store data mining and analysis. International Journal of Applied Information Systems, 12(26):1–5.
Kortum, P. e Sorber, M. (2015). Measuring the usability of mobile applications for phones and tablets. International Journal of Human-Computer Interaction, 31(8):518–529.
Kuspil, J., Ribeiro, J., Leal, G., Guerino, G., e Balancieri, R. (2024). Datasets on mobile app metadata and interface components to support data-driven app design. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS, pages 425–432. INSTICC, SciTePress.
Kuspil, J. C. (2024). Mineração de componentes de interface e metadados em aplicativos móveis. Dissertação de mestrado, Universidade Estadual de Maringá (UEM), Maringá, PR. Programa de Pós-Graduação em Ciência da Computação (PCC–UEM). Orientador: Prof. Dr. Renato Balancieri. Coorientadora: Profa. Dra. Gislaine Camila Lapasini Leal.
Li, K., Xu, Z., e Chen, X. (2014). A platform for searching ui component of android application. In 2014 5th International Conference on Digital Home, pages 205–210. IEEE.
Liu, T. F., Craft, M., Situ, J., Yumer, E., Mech, R., e Kumar, R. (2018). Learning design semantics for mobile apps. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pages 569–579.
McHugh, M. L. (2013). The chi-square test of independence. Biochemia medica, 23(2):143–149.
Moran, K., Bernal-Cárdenas, C., Curcio, M., Bonett, R., e Poshyvanyk, D. (2018). Machine learning-based prototyping of graphical user interfaces for mobile apps. IEEE Transactions on Software Engineering, 46(2):196–221.
Nielsen, J. e Budiu, R. (2015). User Experience for Mobile Applications and Websites. Nielsen Norman Group, Fremont, CA, 3rd edition. Design Guidelines for Improving the Usability of Mobile Sites and Apps; Copyright © Nielsen Norman Group, All Rights Reserved.
Peffers, K., Tuunanen, T., Rothenberger, M. A., e Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3):45–77.
Prakash, G. e Koshy, J. (2021). Google play store apps. [link]. Kaggle. [Acessado 26-Jul-2025].
Pratama, M. e Cahyadi, A. (2020). Effect of user interface and user experience on application sales. In IOP Conference Series: Materials Science and Engineering, volume 879, page 012133. IOP Publishing.
Sahami Shirazi, A., Henze, N., Schmidt, A., Goldberg, R., Schmidt, B., e Schmauder, H. (2013). Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps. In Proceedings of the 5th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS ’13, page 275–284, New York, NY, USA. Association for Computing Machinery.
Sousa, Á. (2019). Coeficiente de correlação de pearson e coeficiente de correlação de spearman: o que medem e em que situações devem ser utilizados? Correio dos Açores: Matemática, page 19. Disponível em [link]. [Acessado 27-Jul-2025].
STATISTA (2023). Statista - technology & telecommunications. [link]. [Acessado 04-Ago-2024].
STATISTA (2024). Statista - number of available applications in the google play store from march 2017 to june 2024. [link]. [Acessado 11-Jun-2025].
Verma, J. P. (2012). Data analysis in management with SPSS software. Springer Science & Business Media.
Wang, B., Li, G., Zhou, X., Chen, Z., Grossman, T., e Li, Y. (2021). Screen2words: Automatic mobile ui summarization with multimodal learning. In The 34th Annual ACM Symposium on User Interface Software and Technology, UIST ’21, page 498–510, New York, NY, USA. Association for Computing Machinery.
Yu, H., Lian, Y., Yang, S., Tian, L., e Zhao, X. (2016). Recommending features of mobile applications for developer. In International Conference on Advanced Data Mining and Applications, pages 361–373. Springer.
Published
2025-09-08
How to Cite
KUSPIL, Jonathan Cesar; LEAL, Gislaine Camila L.; BALANCIERI, Renato.
Mining of Interface Components and Metadata in Mobile Applications. In: BRAZILIAN SYMPOSIUM ON HUMAN FACTORS IN COMPUTATIONAL SYSTEMS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 817-837.
DOI: https://doi.org/10.5753/ihc.2025.12001.
