Open Access
AI-Augmented Paradigms In Enterprise Software Refactoring And Development: A Comprehensive Analysis Of Contemporary Approaches And Theoretical Implications
4
Federal University of Rio de Janeiro, Brazil
Abstract
The accelerating integration of artificial intelligence (AI) in software engineering has transformed both theoretical frameworks and practical methodologies for developing, maintaining, and refactoring enterprise-scale systems. This study examines the evolving landscape of AI-augmented software development with a focus on enterprise monolithic architectures, automation, generative AI tools, and collaborative innovation. Leveraging a synthesis of contemporary literature, the research explores the multifaceted impacts of AI on code quality, deployment efficiency, innovation cycles, and software maintenance strategies. Particular emphasis is placed on the application of AI frameworks to refactor monolithic systems into modular, maintainable, and scalable architectures, as these represent one of the most pressing challenges in contemporary software engineering (Hebbar, 2023). The study further interrogates the intersection of generative AI and model-driven engineering, evaluating transformer-based architectures, reinforcement learning, and graph-based program representations in the context of software development processes (Bouschery et al., 2023; Allamanis et al., 2018). Methodologically, the research adopts an analytical framework that combines comparative literature synthesis with case-based reasoning derived from AI-augmented software deployment practices (Oyeniran et al., 2023; Pashchenko, 2023). The findings reveal that AI integration contributes not only to accelerating the refactoring process but also to enhancing the predictive quality of software systems, optimizing human–machine collaboration, and redefining paradigms of software lifecycle management (Bilgram & Laarmann, 2023; Khankhoje, 2023). The discussion provides a critical evaluation of AI-induced trade-offs, including ethical considerations, quality assurance challenges, and the cognitive demands placed on human developers when interfacing with generative systems. By synthesizing theoretical insights and empirical practices, this study offers a holistic perspective on the future of AI-driven enterprise software engineering and highlights avenues for sustained innovation in automated and semi-automated development ecosystems.
Keywords
Artificial Intelligence,
Software Engineering,
Enterprise Systems,
Monolithic Refactoring
References
Yabaku, M. (2023). Generative AI Tools for Software Engineering: An Analysis of Functionality and Users’ Expectations.
Bilgram, V., & Laarmann, F. (2023). Accelerating innovation with generative AI: AI-augmented digital prototyping and innovation methods. IEEE Engineering Management Review, 51(2), 18-25.
Nagar, G., & Manoharan, A. (2022). Blockchain technology: reinventing trust and security in the digital world. International Research Journal of Modernization in Engineering Technology and Science, 4(5), 6337-6344.
Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer‐based language models. Journal of Product Innovation Management, 40(2), 139-153.
Harman, M., & Jones, B. F. (2001). Search-based software engineering. Information and Software Technology, 43(14), 833-839.
Kishore Subramanya Hebbar. (2023). An AI-Augmented Framework for Refactoring Enterprise Monolithic Systems. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 593–604. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8046.
Nagar, G. (2018). Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, 78-94.
Fontana, F. A., Mäntylä, M. V., Zaytsev, A. V., & Zanoni, I. S. (2016). A large-scale empirical study on the quality of code smell detection tools. ACM Transactions on Software Engineering and Methodology (TOSEM), 25(4), 1-45.
Ozkaya, I. (2023). The next frontier in software development: AI-augmented software development processes. IEEE Software, 40(4), 4-9.
Allamanis, M., Brockschmidt, M., & Khademi, M. (2018). Learning to Represent Programs with Graphs. In International Conference on Learning Representations (ICLR).
Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code. arXiv preprint arXiv:2107.03374.
Pashchenko, D. (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.
Louridas, P. (2023). Generative AI for Software Practitioners.
Oyeniran, O. C., Adewusi, A. O., Adeleke, A. G., Akwawa, L. A., & Azubuko, C. F. (2023). AI-driven devops: Leveraging machine learning for automated software deployment and maintenance. no. December, 2024.
Li, Y., et al. (2022). Competition-Level Code Generation with AlphaCode. Science, 378(6624), 1092-1097.
Bagnato, A., Cicchetti, A., Berardinelli, L., Bruneliere, H., & Eramo, R. (2023). AI-augmented Model-Based Capabilities in the AIDOaRt Project: Continuous Development of Cyber-Physical Systems. ACM SIGAda Ada Letters, 42(2), 99-103.
Hindle, A., Barr, E. T., Su, Z., Gabel, M., & Devanbu, P. (2012). On the naturalness of software. In Proceedings of the 34th International Conference on Software Engineering (ICSE), 837-847.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
Khankhoje, R. (2023). Quality Challenges and Imperatives in Smart AI Software. Computer Science & Information Technology (CS & IT), 143-152.
Nagar, G., & Manoharan, A. (2022). THE RISE OF QUANTUM CRYPTOGRAPHY: SECURING DATA BEYOND CLASSICAL MEANS. 04, 6329-6336.
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