AI-Driven Refactoring Of Enterprise Software: Integrative Frameworks And Emerging Paradigms
Keywords:
Artificial Intelligence, Software Refactoring, Enterprise Systems, Machine LearningAbstract
The evolution of enterprise software architectures has increasingly necessitated robust and scalable methods for system refactoring. Traditional monolithic systems, while foundational in early software engineering, often present challenges related to maintainability, scalability, and integration with contemporary technologies. The advent of artificial intelligence (AI) and machine learning (ML) offers transformative potential to automate, optimize, and augment refactoring processes within complex enterprise environments. This study explores the conceptual foundations, methodological frameworks, and practical implications of AI-augmented refactoring strategies for enterprise software. Building upon Hebbar’s (2023) AI-augmented framework for monolithic system refactoring, the paper systematically evaluates generative AI applications, predictive maintenance, code review automation, and reinforcement learning models as integrated components of software transformation pipelines. The investigation situates these approaches within historical and theoretical contexts, critically examining both technical and ethical dimensions. A comprehensive review of literature emphasizes the nuances of automated decision-making in refactoring, highlighting scholarly debates regarding the efficacy, transparency, and scalability of AI-assisted interventions. Methodologically, this research leverages qualitative synthesis of existing frameworks, comparative analysis of case studies, and theoretical modeling to propose a unified architecture for AI-driven enterprise system refactoring. Findings suggest that AI augmentation not only streamlines structural code transformations but also enhances system resilience, reduces technical debt, and supports continuous evolution in line with dynamic organizational requirements. The study further delineates potential limitations, including data dependency, algorithmic bias, and operational overhead, advocating for iterative human-AI collaboration in the refactoring lifecycle. Implications for practice extend to software engineering governance, strategic IT planning, and ethical deployment of AI in enterprise contexts. By integrating insights from diverse empirical and theoretical sources, this research contributes a comprehensive understanding of the intersection between AI and software refactoring, providing a foundation for future investigations, industry adoption, and policy considerations.
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Copyright (c) 2025 Dr. Maximilian Roth (Author)
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