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@article{197837,
        author = {Ayush Bhandarkar and Pritam Sangole and Rushikesh Dubey and Tushar  Deshmukh and Mukesh Nagrikar and Omkar Dudhbure},
        title = {Intelligent Code Analysis and Error Detection Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6896-6900},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197837},
        abstract = {This study introduces an AI-powered framework for automatic code review and bug detection using the MERN stack integrated with Groq AI API. The proposed system is trained and tested on real-world code samples collected from various sources including GitHub repositories and custom code submissions. To ensure comprehensive analysis, the system supports multiple programming languages including JavaScript, Python, Java, C++, TypeScript, and others. The platform handles real world coding patterns with varying complexity levels, from simple functions to complete project structures. The developed system achieves an overall code review accuracy of approximately 85% and is integrated into a multi user web platform. The platform enables developers to upload code files or complete ZIP projects, obtain AI-generated reviews with confidence visualization, store reports, and share results with team members. Additionally, project administrators can review cases and provide feedback while managing system operations. This work contributes toward bridging the gap between AI based code analysis and practical software development applications by offering an accessible preliminary code screening and quality assessment tool.},
        keywords = {Code Review, Bug Detection, MERN Stack, Groq AI, Automated Code Analysis, Multi-Language Support.},
        month = {April},
        }