AI-Augmented Software Architecture: Autonomous Refactoring with Design Pattern Awareness
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I3P113Keywords:
Software Refactoring, Artificial Intelligence, Design Patterns, Federated Learning, Explainable AI, Legacy Systems, Software ArchitectureAbstract
The maintenance of legacy software systems presents a significant and escalating challenge in software engineering, characterized by high costs, technical debt, and resistance to modernization. This paper introduces an innovative AI-augmented framework for the autonomous refactoring of these systems. Our approach is uniquely centered on the identification of architectural anti-patterns, or ”code smells,” and the subsequent application of appropriate, well-established design patterns to resolve them. The core of our contribution is a hybrid AI model that synergizes Graph Neural Networks (GNNs) for structural code analysis and Transformer-based language models for semantic understanding and code generation. To facilitate collaborative model improvement without compromising proprietary codebases, we propose a novel federated learning framework. This framework is underpinned by a trust metric system that ensures integrity and accountability by weighting contributions from participating silos based on their performance, data distribution, and historical reliability. Furthermore, we address the critical trade-off between model performance and the need for human-understandable outputs by introducing a methodology to quantify and optimize the balance between refactoring efficacy and explainability. We present a comprehensive experimental design and a discussion of hypothetical results, demonstrating our framework’s potential to significantly reduce cyclomatic complexity and improve software maintainability metrics compared to traditional and baseline automated refactoring tools. Our work charts a course toward more intelligent, secure, and transparent software maintenance paradigms
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