AUTOMATED BUG DETECTION AND CORRECTION USING MACHINE LEARNING TECHNIQUE

BSTRACT Software bugs remain a major challenge in software development, often leading to system failures, security vulnerabilities, and increased maintenance costs. Traditional debugging methods are largely manual, time-consuming, and dependent on the expertise of developers. This project presents an Automated Bug Detection and Correction System using Machine Learning techniques to improve efficiency and accuracy in software debugging. The study utilizes supervised machine learning models such as Decision Trees, Random Forest, and Support Vector Machines to detect bugs in source code and suggest possible corrections. A dataset consisting of buggy and corrected code samples was collected and preprocessed to extract meaningful features for model training. The system was designed to analyze code, identify potential errors, and provide correction suggestions with minimal human intervention. The performance of the models was evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the Random Forest model performed best in terms of overall accuracy and reliability. The developed system demonstrates the potential of machine learning in automating debugging processes, improving software quality, and reducing development time. This research contributes to the advancement of intelligent software development tools and provides a foundation for future improvements in AI-driven programming assistance.