Revolutionizing Software Quality: AI-Driven Advanced Code Refactoring and Developer Growth

Abstract: In the rapidly evolving landscape of software development, maintaining high-quality, efficient, and maintainable code has become more critical than ever. Traditional code refactoring techniques, while effective, often require significant manual effort, leading to increased development time and technical debt. This paper explores how artificial intelligence (AI)-driven code refactoring is revolutionizing software quality by automating optimizations, identifying anti-patterns, and suggesting best practices in real time. By leveraging machine learning models, AI-assisted tools can enhance code readability, performance, and security while reducing errors. Furthermore, this paper examines how AI-driven refactoring fosters developer growth by providing intelligent insights, personalized recommendations, and continuous learning opportunities.

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Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-14
DOI
https://doi.org/10.22214/ijraset.2025.70317
Primary Topic
Software Engineering Techniques and Practices
Type
article
Field-Weighted Citation Impact
0.00
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article

Revolutionizing Software Quality: AI-Driven Advanced Code Refactoring and Developer Growth

P eacute rez Joaqu iacute n
International Journal for Research in Applied Science and Engineering Technology
Software Engineering Techniques and Practices
article

Revolutionizing Software Quality: AI-Driven Advanced Code Refactoring and Developer Growth

P eacute rez Joaqu iacute n
article en

Abstract

Abstract: In the rapidly evolving landscape of software development, maintaining high-quality, efficient, and maintainable code has become more critical than ever. Traditional code refactoring techniques, while effective, often require significant manual effort, leading to increased development time and technical debt. This paper explores how artificial intelligence (AI)-driven code refactoring is revolutionizing software quality by automating optimizations, identifying anti-patterns, and suggesting best practices in real time. By leveraging machine learning models, AI-assisted tools can enhance code readability, performance, and security while reducing errors. Furthermore, this paper examines how AI-driven refactoring fosters developer growth by providing intelligent insights, personalized recommendations, and continuous learning opportunities.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Openalex Percentile: Top 86%
Software Engineering Techniques and Practices
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