GEN AI FOR PREDICTIVE CODE REVIEW AND BUG DETECTION

Abstract

Generative Artificial Intelligence for Predictive Code Review and Bug Detection is an intelligent software engineering system developed to automate the process of analyzing source code, identifying bugs, detecting vulnerabilities, and improving code quality. With the rapid growth of software applications and modern development practices, traditional manual code review methods have become time-consuming, error-prone, and highly dependent on developer expertise. This project introduces an AI-powered solution that combines Large Language Models (LLMs) with static code analysis techniques to enhance software quality assurance and reduce development effort. The proposed system utilizes advanced LLM technology through the Groq API powered by the LLaMA 3.3 70B model to perform semantic analysis of source code submitted by users. The AI model understands programming logic, coding patterns, syntax, and contextual relationships within the code to identify bugs, inefficient structures, security vulnerabilities, and coding best practice violations. The system supports multiple programming languages including Python, Java, C, C++, and JavaScript, making it suitable for developers working across different technology domains. In addition to AI-powered analysis, the system integrates Pylint for static code analysis to detect syntax errors, style violations, unused variables, and structural code issues. The combination of Generative AI and static analysis improves the accuracy and reliability of bug detection and code review processes. The application is implemented using Streamlit to provide an interactive web-based interface where users can upload or paste code snippets, select programming languages, and receive detailed analytical feedback instantly.