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ISSN: 2582-8266 (Online)  || UGC Compliant Journal || Google Indexed || Impact Factor: 9.48 || Crossref DOI

Fast Publication within 2 days || Low Article Processing charges || Peer reviewed and Referred Journal

Research and review articles are invited for publication in Volume 18, Issue 2 (February 2026).... Submit articles

Artificial Intelligence based code refactoring

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Swathi Turai, Praneetha Potharaju, Rajasri Aishwarya Bepeta *, Mohammed Adil and Mani Charan Vangala

Department of CSE (Data Science), ACE Engineering College, Hyderabad, Telangana, India.

Review Article

World Journal of Advanced Engineering Technology and Sciences, 2025, 15(02), 639-646

Article DOI: 10.30574/wjaets.2025.15.2.0594

DOI url: https://doi.org/10.30574/wjaets.2025.15.2.0594

Received on 26 March 2025; revised on 02 May 2025; accepted on 04 May 2025

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 It converts jumbled or out-of-date code—such as old Python or Java projects—into more organized, contemporary, and well-documented forms.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.

Legacy Code Refactoring; AI-Powered Code Transformation; Large Language Models (LLMS); Static Code Analysis; Code Optimization; Machine Learning in Software Engineering; Code Maintainability

https://wjaets.com/sites/default/files/fulltext_pdf/WJAETS-2025-0594.pdf

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Swathi Turai, Praneetha Potharaju, Rajasri Aishwarya Bepeta, Mohammed Adil and Mani Charan Vangala. Artificial Intelligence based code refactoring. World Journal of Advanced Engineering Technology and Sciences, 2025, 15(02), 639-646. Article DOI: https://doi.org/10.30574/wjaets.2025.15.2.0594.

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