Model-driven engineering; Android applications; Automatic code generation; Large Language Models
Abstract :
[en] The design process of a mobile Android application commonly begins with the creation of UML diagrams, which represent the structure of the software. However, in many academic and practical scenarios, these diagrams are used solely as project documentation and not as an active part of the software development lifecycle. This leads developers to perform a manual translation, which increases development time and the likelihood of human error. In this work, we propose a program that generates source code for Android applications from UML class diagrams. The tool carries out this process by interpreting standardized UML files in XML format and producing Java code compatible with the Android SDK. This approach aims to facilitate the transition from design to implementation, reduce development effort, and promote the practical use of modeling techniques in software engineering education and practice. Furthermore, we introduce the integration of Large Language Models as a complementary mechanism. By generating structured prompts from UML diagrams, AI-based models can be employed to refine, extend, or suggest additional components of the application. This dual approach—direct code generation combined with AI-assisted refinement—seeks to maximize efficiency, ensure consistency between models and implementation, and highlight the potential of combining Model-Driven Engineering with AI-powered code generation.
Disciplines :
Computer science
Author, co-author :
Macias, Dana; Universidad Distrital Francisco José de Caldas
GREVISSE, Christian ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) > Medical Education
Florez, Hector; Universidad Distrital Francisco José de Caldas
External co-authors :
yes
Language :
English
Title :
Towards a Model-driven Approach to Automatic Code Generation for Android Applications
Publication date :
October 2025
Event name :
4th International Workshop on Systems Modeling (WSM)
Event date :
08-10-2025
Audience :
International
Main work title :
Joint Proceedings of the ICAI 2025 Workshops WAAI 2025, AIESD 2025, WDEA 2025, WKMIT 2025, SCTSD 2025, WSM 2025 co-located with 8th International Conference on Applied Informatics (ICAI 2025)
J. Rumbaugh, I. Jacobson, G. Booch, The Unified Modeling Language Reference Manual, (2nd Edition), Pearson Higher Education, 2004.
E. Hernández Orallo, El lenguaje unificado de modelado (uml), Acta de Informática y Computación 026067 (2022). URL: https://www.acta.es/medios/articulos/informatica_y_computacion/026067.pdf.
D. Sanchez, Leveraging a model transformation chain for semi-automatic source code generation on the android platform., in: ICAI Workshops, 2023, pp. 150–164.
H. Florez, E. Garcia, D. Muñoz, Automatic code generation system for transactional web applications, in: Computational Science and Its Applications–ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V 19, Springer, 2019, pp. 436–451. doi:10.1007/978-3-030-24308-1_36.
D. Sobania, M. Briesch, C. Hanna, J. Petke, An analysis of the automatic bug fixing performance of chatgpt, in: 2023 IEEE/ACM International Workshop on Automated Program Repair (APR), IEEE, 2023, pp. 23–30. doi:10.1109/APR59189.2023.00012.
J. Jiang, F. Wang, J. Shen, S. Kim, S. Kim, A survey on large language models for code generation, ACM Trans. Softw. Eng. Methodol. (2025). doi:10.1145/3747588.
H. Zafar, S. Ur Rehman Khan, A. Mashkoor, H. U. Nisa, Mobicat: a model-driven engineering approach for automatic gui code generation for android applications, Frontiers in Computer Science 6 (2024). doi:10.3389/fcomp.2024.1397805.
H. Florez, M. Leon, Model driven engineering approach to configure software reusable components, in: Applied Informatics: First International Conference, ICAI 2018, Bogotá, Colombia, November 1-3, 2018, Proceedings 1, Springer, 2018, pp. 352–363. doi:10.1007/978-3-030-01535-0_26.
D. Sanchez, H. Florez, Model driven engineering approach to manage peripherals in mobile devices, in: Computational Science and Its Applications–ICCSA 2018: 18th International Conference, Melbourne, VIC, Australia, July 2–5, 2018, Proceedings, Part IV 18, Springer, 2018, pp. 353–364. doi:10.1007/978-3-319-95171-3_28.
J. Bézivin, On the unification power of models, Software & Systems Modeling 4 (2005) 171–188. doi:10.1007/s10270-005-0079-0.
A. G. Parada, L. B. De Brisolara, A model driven approach for android applications development, in: 2012 Brazilian Symposium on Computing System Engineering, IEEE, 2012, pp. 192–197. doi:10. 1109/SBESC.2012.44.
D. Sanchez, A. E. Rojas, H. Florez, Towards a clean architecture for android apps using model transformations, IAENG International Journal of Computer Science 49 (2022) 270–278.
R. F. García, MVVM: Model–View–ViewModel, in: iOS Architecture Patterns: MVC, MVP, MVVM, VIPER, and VIP in Swift, Apress, Berkeley, CA, 2023, pp. 145–224. doi:10.1007/ 978-1-4842-9069-9_4.
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, E. Brynjolfsson, S. Buch, D. Card, R. Castellon, N. Chatterji, A. Chen, K. Creel, J. Q. Davis, D. Demszky, C. Donahue, M. Doumbouya, E. Durmus, S. Ermon, J. Etchemendy, K. Ethayarajh, L. Fei-Fei, C. Finn, T. Gale, L. Gillespie, K. Goel, N. Goodman, S. Grossman, N. Guha, T. Hashimoto, P. Henderson, J. Hewitt, D. E. Ho, J. Hong, K. Hsu, J. Huang, T. Icard, S. Jain, D. Jurafsky, P. Kalluri, S. Karamcheti, G. Keeling, F. Khani, O. Khattab, P. W. Koh, M. Krass, R. Krishna, R. Kuditipudi, A. Kumar, F. Ladhak, M. Lee, T. Lee, J. Leskovec, I. Levent, X. L. Li, X. Li, T. Ma, A. Malik, C. D. Manning, S. Mirchandani, E. Mitchell, Z. Munyikwa, S. Nair, A. Narayan, D. Narayanan, B. Newman, A. Nie, J. C. Niebles, H. Nilforoshan, J. Nyarko, G. Ogut, L. Orr, I. Papadimitriou, J. S. Park, C. Piech, E. Portelance, C. Potts, A. Raghunathan, R. Reich, H. Ren, F. Rong, Y. Roohani, C. Ruiz, J. Ryan, C. Ré, D. Sadigh, S. Sagawa, K. Santhanam, A. Shih, K. Srinivasan, A. Tamkin, R. Taori, A. W. Thomas, F. Tramèr, R. E. Wang, W. Wang, B. Wu, J. Wu, Y. Wu, S. M. Xie, M. Yasunaga, J. You, M. Zaharia, M. Zhang, T. Zhang, X. Zhang, Y. Zhang, L. Zheng, K. Zhou, P. Liang, On the opportunities and risks of foundation models, 2022. URL: https://arxiv.org/abs/2108.07258. doi:10.48550/arXiv.2108.07258. arXiv:2108.07258.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language models are few-shot learners, in: H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin (Eds.), Advances in Neural Information Processing Systems, volume 33, Curran Associates, Inc., 2020, pp. 1877–1901. URL: https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, G. Neubig, Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, ACM Computing Surveys 55 (2023) 1–35. doi:10.1145/3560815.
H. S. Son, W. Y. Kim, R. Y. C. Kim, Mof based code generation method for android platform, International Journal of Software Engineering and Its Applications 7 (2013) 415–426.
A. Nirumand Jazi, I. Alfonso, J. Cabot, Low-code flutter application development solution, in: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, MODELS Companion’24, Association for Computing Machinery, New York, NY, USA, 2024, p. 838–847. doi:10.1145/3652620.3688330.
E. A. Team, Atl: A model transformation tool, 2025. URL: https://help.eclipse.org/latest/topic/org.eclipse.m2m.atl.doc/guide/user/ATL%20User%20Guide.html, accessed: 2025-09-07.
O. M. G. (OMG), Qvt - mof query/view/transformation, 2016. URL: https://www.omg.org/spec/QVT, accessed: 2025-09-07.
B. Team, Prompt engineering: Best practices for 2025, 2025. URL: https://www.bridgemind.ai/blog/ prompt-engineering-best-practices, accessed: 2025-09-07.
M. Carli, Uml for android engineers, 2021. URL: https://www.kodeco.com/21792733-uml-for-android-engineers/page/3, accessed: 2025-09-07.
