Integrating Machine Learning and Model-Driven Engineering for User Interfaces: A Systematic Literature Review

• Pedro Aguilar-Encarnacion Escuela Politécnica Nacional
• Carlos Iñiguez-Jarrín Escuela Politécnica Nacional
• Julio Sandobalín Escuela Politécnica Nacional

Resumo

User Interfaces (UI) operate in heterogeneous contexts and require adaptation and, in some cases, intelligent behavior. Machine Learning (ML) and Model-Driven Engineering (MDE) offer complementary capabilities to address this challenge; however, the evidence on their combined use in UI development remains fragmented. The literature still lacks an integrated synthesis of how ML and MDE are combined in this domain. This study aims to synthesize this evidence through a Systematic Literature Review conducted according to the guidelines of Kitchenham, Budgen, and Klotins. The findings cover UI typologies, ML roles and model families, MDE artefacts and paradigms, ML–MDE integration workflows, and coupling, and identify research gaps.

Referências

Abrahão, S., Insfran, E., Sluÿters, A., and Vanderdonckt, J. (2021). Model-based intelligent user interface adaptation: challenges and future directions. Software and Systems Modeling, 20(5):1335 – 1349.

Ahmed, A., Azab, S., Moussa, S. M., and Abdelhamid, Y. (2025). Vit-dtc: vision transformer-based design-to-code framework for code generation from generated ui designs and hand-drawn sketches. Neural Computing and Applications, 37(29):24243 – 24264.

Akiki, P. A., Bandara, A. K., and Yu, Y. (2014). Adaptive model-driven user interface development systems. ACM Comput. Surv., 47(1):1 – 33.

Almonte, L., Guerra, E., Cantador, I., and de Lara, J. (2022). Recommender systems in model-driven engineering: A systematic mapping review. Softw. Syst. Model., 21(1):249–280.

Alti, A. and Lakehal, A. (2025). Ai-mdd-ux: Revolutionizing e-commerce user experience with generative ai and model-driven development. Future Internet, 17(4):1 – 34.

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., and Zimmermann, T. (2019). Software engineering for machine learning: a case study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP ’19, page 291–300. IEEE Press.

Budgen, D., Turner, M., Brereton, P., and Kitchenham, B. (2008). Using mapping studies in software engineering. In Proceedings of PPIG 2008, pages 195–204. Lancaster University.

Cardona-Reyes, H., Muñoz-Arteaga, J., Mitre-Ortiz, A., and Villalba-Condori, K. O. (2021). Model-driven approach of virtual interactive environments for enhanced user experience. Applied Sciences, 11(6):2804.

Criado, J., Vicente-Chicote, C., Padilla, N., and Iribarne, L. (2010). A model-driven approach to graphical user interface runtime adaptation. In Proceedings of the 5th Workshop on [e-mail] at the 13th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2010), volume 641 of [e-mail], pages 49–59. CEUR-WS.

Da Silva, M. R., Leite, L. L., and Garcia Costa Dos Santos, G. L. (2022). Algorithms for the development of adaptive web interfaces: A systematic literature review. In 2022 XVII Latin American Conference on Learning Technologies (LACLO). IEEE.

Fernandez-Garcia, A. J., Iribarne, L., Corral, A., and Wang, J. Z. (2015). Evolving mashup interfaces using a distributed machine learning and model transformation methodology. In On the Move to Meaningful Internet Systems: OTM 2015 Workshops, volume 9416 of Lecture Notes in Computer Science, pages 401–410. Springer, Cham.

Gaspar-Figueiredo, D., Fernández-Diego, M., Nuredini, R., Abrahao, S., and Insfran, E. (2024). Reinforcement learning-based framework for the intelligent adaptation of user interfaces. In Companion Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS ’24 Companion, page 40–48, New York, NY, USA. Association for Computing Machinery.

Genero Bocco, M., Piattini Velthuis, M. G., Cruz-Lemus, J. A., and Díaz García, Ó. (2023). Métodos de Investigación en Informática. AQC Lab.

Kitchenham, B. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report Version 2.3, Keele University, UK.

Kitchenham, B., Brereton, P., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and Software Technology, 51(1):7–15.

Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. Chapman and Hall/CRC, 1 edition.

Klotins, E., Unterkalmsteiner, M., and Gorschek, T. (2015). Software engineering knowledge areas in startup companies: A mapping study. In Fernandes, J. M., Machado, R. J., and Wnuk, K., editors, Software Business, pages 245–257, Cham. Springer International Publishing.

Kristić, M., Zakarija, I., Škopljanac-Mačina, F., and Car, Ž. (2025). Machine learning for adaptive accessible user interfaces: Overview and applications. Applied Sciences, 15(23):12538.

Maybury, M. (1999). Intelligent user interfaces: An introduction. In Proceedings of the 4th International Conference on Intelligent User Interfaces, IUI ’99, pages 3–4, New York, NY, USA. Association for Computing Machinery.

McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3):276 – 282.

Mejias, J. C., Silega, N., Noguera, M., Rogozov, Y. I., and Lapshin, V. S. (2022). Model-driven user interface development: A systematic mapping. In Agredo-Delgado, V., Ruiz, P. H., and Correa-Madrigal, O., editors, Human-Computer Interaction, pages 114–129, Cham. Springer International Publishing.

Mezhoudi, N. and Vanderdonckt, J. (2021). Toward a task-driven intelligent gui adaptation by mixed-initiative. International Journal of Human-Computer Interaction, 37(5):445 – 458.

Naveed, H., Arora, C., Khalajzadeh, H., Grundy, J., and Haggag, O. (2024). Model driven engineering for machine learning components: A systematic literature review. Information and Software Technology, 169:107423.

Omar, K. and Gómez, J. M. (2025). Intelligent user interfaces for metaverse. In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE.

Peters, M. D. J., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., and Khalil, H. (2020). Chapter 11: Scoping reviews (2020 version). In Aromataris, E. and Munn, Z., editors, JBI Manual for Evidence Synthesis. JBI.

Planas, E., Daniel, G., Brambilla, M., and Cabot, J. (2021). Towards a model-driven approach for multiexperience ai-based user interfaces. Software and Systems Modeling, 20(4):997 – 1009.

Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE ’14, pages 1–10, New York, NY, USA. Association for Computing Machinery.