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Research Paper | Computer Science and Information Technology | United States of America | Volume 14 Issue 1, January 2025 | Popularity: 4.6 / 10
AI-Driven Automatic Code Refactoring for Performance Optimization
Omkar Reddy Polu
Abstract: Code refactoring is an important practice to improve software maintainability, readability, performance in general. Current refactoring approaches are mainly based on a manual intervention thus makes it time consuming and error prone. With the rising AI driven solutions, the way came to implement enhanced performance with acceptable quality of code has become feasible through automatic code refactoring. This research takes a look at an AI - based framework to refactor the code automatically based on deep learning models, reinforcement learning, and symbolic analysis for identifying inefficiencies and optimizing the code structure. We developed our approach that is an integration of a hybrid AI model capable of static and dynamic analysis in order to look for bottlenecks and apply performance enhancing transformations. Then, we introduce an intelligent refactoring engine leveraging transformer-based models and graph neural networks (GNNs) to learn code semantics and gastrointestinal surgery what might be the best restructuring strategy. Moreover, our system by us iteratively refines refactored code using reinforcement learning, given the execution performance. Relevant to the research also included the use of AI to optimize memory usage, efficiency in time complexity, and computational efficiency while maintaining functional correctness. Performance gains and maintainability benefits are demonstrated on real world open-source repositories that are used as empirical evaluations. The use of AI driven automation in software engineering is brought up by this study which will eventually lead to more efficient, scalable, and high-performance software development processes.
Keywords: AI - driven Code Refactoring, Automatic Code Optimization, Software Performance Enhancement, Deep Learning for Code Analysis, Graph Neural Networks (GNNs) in Refactoring
Edition: Volume 14 Issue 1, January 2025
Pages: 1316 - 1320
DOI: https://www.doi.org/10.21275/SR25011114610
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