Artificial Intelligence based code refactoring

Turai, Swathi and Potharaju, Praneetha and Bepeta, Rajasri Aishwarya and Adil, Mohammed and Vangala, Mani Charan (2025) Artificial Intelligence based code refactoring. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 639-646. ISSN 2582-8266

One of the most difficult aspects of software development is maintaining and updating legacy code, which frequently requires a significant investment of time and energy to make the code more manageable, efficient, and readable. Using sophisticated AI, such as machine learning and large language models, the AI-Powered Codebase Refactorer is a clever tool made to make this process easier. It converts jumbled or out-of-date code—such as old Python or Java projects—into more organized, contemporary, and well-documented forms. The tool makes the code much easier to understand by adding useful comments and producing external API documentation in addition to applying best practices like modularization and design patterns to improve code structure. In order to make sure the code continues to function properly after changes, it uses automated tests and static analysis, which goes beyond simply tidying up syntax. Whether it's for system software, data tools, or web apps, this AI modifies its methodology to suit the particular project. Developers can reduce technical debt, save time, and maintain the functionality of critical software by automating a large portion of the refactoring process.