Published Paper Details:

The rapid advancement of software development technologies has significantly increased the complexity and scale of modern codebases. As a result, debugging and code comprehension have become more time-consuming and error-prone, particularly for novice programmers. This paper presents a novel AI-powered system designed to provide real-time code debugging, intelligent suggestions, and personalized learning support. Leveraging machine learning models, natural language processing, and static/dynamic code analysis techniques, the proposed assistant can detect common coding errors, offer context-aware recommendations, and explain programming concepts interactively. The system is integrated into popular development environments, enabling seamless and intuitive user interaction. Through extensive testing across various programming languages and real-world scenarios, our tool demonstrates improved debugging efficiency, faster learning curves, and reduced dependency on external documentation. This research contributes to the evolution of intelligent programming assistants, aiming to bridge the gap between coding education and professional development practices.