An AI-Augmented Framework for Refactoring Enterprise Monolithic Systems
Keywords:
Legacy system modernization, Code refactoring, Monolithic architectures, Microservice migration, AI-assisted software engineering, Enterprise application evolutionAbstract
Many large organizations still depend on legacy monolithic systems that were built over many years and now hold deeply embedded business logic. Moving these systems to cloud-native microservices is widely desired, but the process is slow, risky, and heavily dependent on manual code understanding, which often leads to errors and rework. Current migration approaches either rely on rigid rule-based tools or expect full manual refactoring, leaving a gap in practical support for understanding complex dependencies and identifying safe service boundaries. The goal of this study is to address this gap by providing intelligent, decision-oriented assistance that helps engineers refactor legacy code while preserving existing business behavior. The proposed approach introduces an AI-augmented modular refactoring framework that combines static code analysis, dependency graph modeling, and machine learning-based pattern recognition. Instead of automatically rewriting code, the framework highlights logical decomposition points, detects refactoring candidates, and surfaces architectural risks. A human-in-the-loop workflow allows developers and architects to review, adjust, and validate recommendations before changes are applied, supporting incremental migration rather than disruptive rewrites. Evaluation on enterprise-scale legacy applications shows a reduction of refactoring effort by approximately 25 to 40 percent compared to fully manual approaches. The resulting microservices also exhibit improved modularity and fewer post-migration defects during validation. This framework can be applied to large enterprise modernization initiatives where reliability and domain integrity are critical. By combining human expertise with AI-assisted insight, the work demonstrates a practical and novel way to reduce risk and effort in legacy-to-microservice migration.
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