AI-Driven Code Optimization and Refactoring for Large-Scale Software Development
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Abstract
AI-driven code optimization and refactoring have emerged as transformative approaches in large-scale software development, offering significant improvements in both performance and maintainability. As software systems grow in complexity and size, managing and optimizing the underlying code becomes increasingly challenging. Traditional optimization techniques, while effective, are often time-consuming and require deep expertise. The application of Artificial Intelligence (AI) in this domain enables automated analysis, identification of inefficiencies, and the generation of optimal code solutions in a fraction of the time. By leveraging machine learning models, AI can predict and refactor code patterns, optimizing not only performance but also ensuring maintainability and scalability of the software. This paper explores various AI techniques, including deep learning, reinforcement learning, and natural language processing, in automating code refactoring processes. The study delves into the benefits of integrating AI-driven systems with existing development frameworks, emphasizing the potential for increased developer productivity, reduced errors, and enhanced system performance. Additionally, the challenges associated with implementing AI for large-scale systems, such as data dependency and model interpretability, are discussed. The growing role of AI in code optimization promises to shape the future of software development by significantly reducing manual intervention while maintaining high standards of code quality
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