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
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