AI driven defect prediction and automated refactoring framework for large scale software systems

The importance of intelligent solutions that enhance software stability, maintainability, quality, and dependability has grown in recent years due to the increasing complexity of software systems. Conventional approaches to fault prediction and code rearrangement encounter scalability problems when confronted with large-scale, dynamic circumstances. The most recent advancements in automated refactoring frameworks and AI-driven fault prediction are examined in this review paper. In order to detect, prevent, and fix software problems before they happen, these frameworks use ML, DL, and NLP. Techniques to source code analysis, feature extraction, and quality improvement that rely on AI models are examined critically, along with emerging trends, methodology, and tools. Code structure optimization and automated refactoring decisions with minimal human interaction may be possible with the help of graph neural networks, RL, and predictive analytics, according to research. In addition, it explores the challenges of scalability, data quality, model interpretability, and integration with CI/CD workflows. Finally, the report concludes with research recommendations for future studies that could provide explainable, adaptive, and domain-independent frameworks to make software maintenance an autonomous, self-improving process.