AI-Assisted Multi-Language Code Bug Detection and Auto-Fixing System

Abstract

Software development is an inherently complex process where bugs and errors can significantly impact performance, security, and usability. Detecting and fixing these issues early in the development lifecycle is crucial to reducing development costs and improving software quality. This project presents an AI-Assisted Multi-Language Code Bug Detection and Auto-Fixing System, a desktop-based application developed using Python and Tkinter that provides intelligent debugging support for Python, C, and Java programs. The system integrates static code analysis, heuristic-based artificial intelligence techniques, and external linting tools to identify potential bugs, syntax errors, and bad programming practices. For Python programs, the application leverages the Abstract Syntax Tree (AST) module to perform syntax validation, ensuring that code structure adheres to language rules. Additionally, the system incorporates a heuristicbased AI bug prediction module that detects common programming mistakes such as infinite loops, improper exception handling, misuse of comparison operators, and outdated syntax usage. To enhance the robustness of the analysis, the tool integrates with Pylint, a widely used static code analysis tool, to generate detailed error and warning reports. These reports provide insights into code quality, helping developers adhere to coding standards and best practices. For C and Java languages, the system performs basic structural checks to identify missing essential components such as print statements and class declarations. A key feature of the proposed system is its Auto-Fix module, which automatically corrects simple syntax issues such as missing colons and trailing whitespace. This feature reduces manual debugging effort and improves developer productivity. The application also includes a user-friendly graphical interface with code editing capabilities, syntax highlighting, and real-time error visualization, making it suitable for both beginners and experienced programmers. The system is designed to be extensible, allowing future integration of advanced machine learning models for more accurate bug prediction and automated code repair. By combining traditional static analysis with AI-driven techniques, this project contributes to the development of intelligent programming tools that assist developers in writing clean, efficient, and error-free code. Overall, this project demonstrates how intelligent debugging systems can enhance software development processes by providing automated assistance, reducing human errors, and improving code reliability. It serves as a practical solution for academic learning as well as real-world software engineering applications.