Review highlights benefits of AI-powered debugging tools in logical error identification and syntax analysis, suggesting significant improvements in programming education.
The AI-powered code debugging and explainer has emerged as a promising approach to assist programmers beyond traditional syntax error detection. Prior studies explore conversational debuggers, prompt-based logical error correction, neural machine translation for bug fixing, and educational tools that diagnose common student errors. However, limitations such as dependency on third-party APIs, lack of offline functionality, poor explainability of fixes, and restricted language support remain significant challenges. An AI-powered Code Debugger and Explainer aims to identify both syntax and logical errors across multiple programming languages while providing clear, human-readable explanations. By emphasizing interpretability, accessibility, and educational support, the system seeks to improve debugging efficiency and enhance conceptual understanding for students, educators, and software developers.
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Sufyan et al. (2025) studied this question.
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