AI-Driven Code Refactoring: Improving Java Backend Code Quality with Machine Learning Models
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Abstract
Code refactoring is essential for maintaining high-quality, maintainable, and scalable software systems. However, manual refactoring of large-scale Java backend codebases is a time-consuming and error-prone task. This paper introduces an AI-driven approach to automate code refactoring using machine learning models, aiming to improve the quality, readability, and performance of Java backend applications. The proposed framework utilizes supervised learning techniques to analyze code metrics, detect code smells, and recommend refactoring actions based on patterns identified from a vast dataset of well-structured code. By employing deep learning models, such as neural networks, the system can also predict the impact of proposed changes on system performance, enabling developers to make informed decisions. A case study demonstrates the effectiveness of the model in refactoring Java applications, leading to a measurable reduction in technical debt, improved code structure, and enhanced maintainability. This approach not only accelerates the refactoring process but also ensures consistent and reliable improvements in code quality, thus enabling better long-term management of backend systems.
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References
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