Vol. 1 No. 2 (2025)

Articles

Theory and Practice of AI-Augmented Software Development in Europe

Published 2025-10-26

  • Denis S. Pashchenko

Denis S. Pashchenko
Independent Researcher, Moscow 123000, Russia

DOI: https://doi.org/10.64797/rwas.v1i2.104

Abstract

This article explores the practical application of artificial intelligence (AI) tools (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.

Keywords

AI, Software Engineering, Large Language Model (LLM), Android, ChatGPT

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References

  1. [1] Gorban, A.N., Grechuk, B., Tyukin, I.Y., 2018. Augmented Artificial Intelligence: A Conceptual Framework. arXiv preprint. arXiv:1802.02172. DOI: https://doi.org/10.48550/arXiv.1802.02172
  2. [2] Kästner, C., Kang, E., 2020. Teaching Software Engineering for AI-Enabled Systems. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training, online, 23–29 May 2020; pp. 45–48. DOI: https://doi.org/10.1145/3377814.3381714
  3. [3] Barenkamp, M., Rebstadt, J., Thomas, O., 2020. Applications of AI in Classical Software Engineering. AI Perspectives. 2, 1. DOI: https://doi.org/10.1186/s42467-020-00005-4
  4. [4] Boyko, O., Holoborodko, Y., 2024. Distributed Software Development in 2025: All You Should Know. Available from: https://spd.tech/dedicated-development-teams/distributed-software-development-team/ (cited 24 August 2025).
  5. [5] Pashchenko, D.S., 2024. Consolidation of a New Organizational and Production Paradigm in Software Development Projects. Information Technologies. 3, 150–158. DOI: https://doi.org/10.17587/it.30.150-158
  6. [6] Panetta, K., 2023. Set Up Now for AI to Augment Software Development. Gartner: Stamford, CT, USA.
  7. [7] Cakmak, Z., 2023. Adapting to Environmental Change: The Importance of Organizational Agility in the Business Landscape. Florya Chronicles of Political Economy. 9(1), 67–87. DOI: https://doi.org/10.17932/IAU.FCPE.2015.010/fcpe_v09i1004
  8. [8] 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. DOI: https://doi.org/10.5815/ijitcs.2023.06.03
  9. [9] Pashchenko, D.S., 2025. Growing Demand for Artificial Intelligence Tools in Software Engineering: Results of a Pan-European Study 2024. Revista de Investigación en Tecnologías de la Información. 13(29), 82–91. DOI: https://doi.org/10.36825/RITI.13.29.008 (in Spanish)
  10. [10] Misra, S., Kumar, V., Kumar, U., et al., 2012. Agile Software Development Practices: Evolution, Principles, and Criticisms. International Journal of Quality and Reliability Management. 29, 972–980.
  11. [11] Barai, T.V., Prasad, E., 2023. Smartwatch Market (2023–2032). Available from: https://www.alliedmarketresearch.com/smartwatch-market (cited 24 August 2025).
  12. [12] Ahlgren, M., 2023. OpenAI Statistics and Facts for 2023 (DALL·E, ChatGPT and GPT-3.5). Websiterating: Sunshine Coast, Australia.
  13. [13] Rogers, E.M., 2003. Diffusion of Innovations, 5th ed. Simon & Schuster: New York, NY, USA.
  14. [14] Wierda, G., 2021. Mastering ArchiMate Edition 3.1: A Serious Introduction to the ArchiMate Enterprise Architecture Modeling Language. R&A: Heerlen, The Netherlands.
  15. [15] Sommerville, I., 2009. Software Engineering, 9th ed. Addison-Wesley: Boston, MA, USA.
  16. [16] Chuah, S.H.-W., Rauschnabel, P.A., Krey, N., et al., 2016. Wearable Technologies: The Role of Usefulness and Visibility in Smartwatch Adoption. Computers in Human Behavior. 65, 276–284. DOI: https://doi.org/10.1016/j.chb.2016.07.047
  17. [17] Sattelberg, W., 2021. Google is Updating App Guidelines for Smartwatches Just in Time for Wear OS. Available from: https://www.androidpolice.com/2021/09/03/google-is-updating-app-guidelines-for-smartwatches-just-in-time-for-wear-os-3/ (cited 24 August 2025).
  18. [18] Glinka, F., Raed, A., Gorlatch, S., 2010. A Service-Oriented Interface for Highly Interactive Distributed Applications. In: Lin, H.-X., Alexander, M., Forsell, M., et al. (Eds.). Euro-Par 2009—Parallel Processing Workshops. Springer: Berlin/Heidelberg, Germany. pp. 266–277. DOI: https://doi.org/10.1007/978-3-642-14122-5_31
  19. [19] Ferdiana, R., 2024. The Impact of Artificial Intelligence on Programmer Productivity. In Proceedings of the International Conference on Software Engineering and Information Technology (ICOSEIT) 2024, Bandung, Indonesia, 28–29 February 2024; pp. 1–6. Available from: https://www.researchgate.net/publication/378962192_The_Impact_of_Artificial_Intelligence_on_Programmer_Productivity
  20. [20] Finio, M., Downie, A., 2024. AI in software development. Available from: https://www.ibm.com/es-es/think/topics/ai-in-software-development (cited 24 August 2025). (in Spanish)
  21. [21] Peng, S., Kalliamvakou, E., Cihon, P., et al., 2023. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. Available from: https://arxiv.org/pdf/2302.06590.pdf (cited 24 August 2025).
  22. [22] Petrovic, N., Lebioda, K., Zolfaghari, V., et al., 2024. LLM-Driven Testing for Autonomous Driving Scenarios. In Proceedings of the 2024 2nd International Conference on Foundation and Large Language Models, Dubai, United Arab Emirates, 26–29 November 2024; pp. 173–178.
  23. [23] Naimi, L., Bouziane, E.M., Jakimi, A., et al., 2024. Automating Software Documentation: Employing LLMs for Precise Use Case Description. Procedia Computer Science. 246, 1346–1354. DOI: https://doi.org/10.1016/j.procs.2024.09.568
  24. [24] Kurnianingrum, D., Jumbri, I.A., Ratnapuri, C.I., et al., 2024. Exploring the ChatGPT’s Impact and Prospects for Business Research Purposes. In Proceedings of the 2024 3rd International Conference for Innovation in Technology (INOCON), Bangalore, India, 18–20 April 2024; pp. 1–6. DOI: https://doi.org/10.1109/INOCON60754.2024.10511483
  25. [25] Cao, S., Huang, C.-M., 2022. Understanding User Reliance on AI in Assisted Decision-Making. Proceedings of the ACM on Human-Computer Interaction. 6, 471.