Theory and practice of AI-augmented software development in Europe in 2025
DOI:
https://doi.org/10.71112/pahqyx95Keywords:
AI, software engineering, LLM, Android, ChatGPTAbstract
This article explores the practical application of AI tool (ChatGPT v3.5 and 4) in the full lifecycle of a real-world software development project, based on field research of 2023-2024 in Europe. The research evaluates the effectiveness of AI in generating core software artifacts, including functional specifications, software code, automated tests, user documentation, and application content. While the AI tool proved to be a powerful assistant for creating and refining these deliverables, its limitations became evident in areas requiring up-to-date technical guidance or creative input in product business logic development. In general, ChatGPT significantly accelerated the development process, with project team members reporting a substantial increase in productivity. The study also highlights best practices for AI usage in software engineering, emphasizing the importance of service-oriented design, iterative prompt refinement, and ongoing human oversight. Despite its shortcomings in generating original ideas or adapting to evolving platform requirements, ChatGPT demonstrated strong capabilities in automating repetitive tasks and enhancing overall efficiency. The findings confirm the research hypothesis that AI can reliably produce key software development artifacts with minimal human input, marking a pivotal step toward the broader integration of AI in software engineering practices.
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References
Ahlgren, M. (2023). OpenAI statistics & facts for 2023 (DALL·E, ChatGPT & GPT-3.5). Website Rating. https://www.websiterating.com/research/openai-statistics/
Barai, T. V., & Mutreja, S. (2023). Smartwatch market: Global opportunity analysis and industry forecast, 2023–2032. Allied Market Research. https://www.alliedmarketresearch.com/smartwatch-market
Barenkamp, M., Rebstadt, J., & Thomas, O. (2020). Applications of AI in classical software engineering. AI Perspectives, 2, Article 1. https://doi.org/10.1186/s42467-020-00005-4 DOI: https://doi.org/10.1186/s42467-020-00005-4
Boyko, O., & Holoborodko, Y. (2024). Distributed software development in 2025: All you should know. SPD Tech Blog. https://spd.tech/dedicated-development-teams/distributed-software-development-team/
Cakmak, Z. (2023). Adapting to environmental change: The importance of organizational agility in the business landscape. Florya Chronicles of Political Economy, 9, 42–53. https://doi.org/10.17932/IAU.FCPE.2015.010/fcpe_v09i1004 DOI: https://doi.org/10.17932/IAU.FCPE.2015.010/fcpe_v09i1004
Glinka, F., & Raed, A. (2009). A service-oriented interface for highly interactive distributed applications. En Lecture Notes in Computer Science (Vol. 6043, pp. 266–277). Springer. https://doi.org/10.1007/978-3-642-14122-5_31 DOI: https://doi.org/10.1007/978-3-642-14122-5_31
Gorban, A., Grechuk, B., & Tyukin, I. (2018). Augmented artificial intelligence: A conceptual framework. arXiv. https://arxiv.org/abs/1802.02172
Kästner, C., & Kang, E. U. (2020). Teaching software engineering for AI-enabled systems. En Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training (pp. 45–48). ACM. https://doi.org/10.1145/3377814.3381714 DOI: https://doi.org/10.1145/3377814.3381714
Misra, S. C., Kumar, V., Kumar, U., Fantazy, K. A., & Akhter, M. (2012). Agile software development practices: Evolution, principles, and criticisms. International Journal of Quality & Reliability Management, 29, 972–980. DOI: https://doi.org/10.1108/02656711211272863
Panetta, K. (2023). Set up now for AI to augment software development. Gartner. https://www.gartner.com/en/articles/set-up-now-for-ai-to-augment-software-development
Pashchenko, D. S. (2023). Early formalization of AI-tools usage in software engineering in Europe: Study of 2023. International Journal of Information Technology and Computer Science, 15(6), 29–36. https://doi.org/10.5815/ijitcs.2023.06.03 DOI: https://doi.org/10.5815/ijitcs.2023.06.03
Pashchenko, D. S. (2024). Consolidation of a new organizational and production paradigm in software development projects. Information Technologies, (3), 150–158. https://doi.org/10.17587/it.30.150-158 DOI: https://doi.org/10.17587/it.30.150-158
Pashchenko, D. S. (2025). Crecimiento en la demanda de herramientas de inteligencia artificial en ingeniería de software: Resultados de un estudio paneuropeo 2024. Revista de Investigación en Tecnologías de la Información, 13(29), 82–91. https://doi.org/10.36825/RITI.13.29.008 DOI: https://doi.org/10.36825/RITI.13.29.008
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon & Schuster.
Sattelberg, W. (2023). Google is updating app guidelines for smartwatches just in time for Wear OS. Android Police. https://www.androidpolice.com/2021/09/03/google-is-updating-app-guidelines-for-smartwatches-just-in-time-for-wear-os-3/
Wierda, G. (2021). Mastering ArchiMate edition 3.1: A serious introduction to the ArchiMate enterprise architecture modeling language. R&A.
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Copyright (c) 2026 Denis Svyatoslavovich Pashchenko (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
