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@article{187955,
        author = {Rekha B K and Dr. Anitha T N and Moulya M and Pranavi C and Kruthi CB},
        title = {ENHANCING SOFTWARE COMPOSITION ANALYSIS (SCA) TOOLS USING CREW AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {575-579},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187955},
        abstract = {Current Software Composition Analysis (SCA) tools primarily focus on detecting known vulnerabilities within software dependencies but lack the capability to provide a unified, intelligent, and predictive view of how these dependencies interact and affect the underlying codebase. This paper introduces a next-generation SCA framework powered by Crew AI, designed to overcome these limitations by enabling multi-agent collaboration for deeper and more accurate analysis. The proposed system integrates real-time GitHub connectivity, comprehensive dependency mapping, and predictive impact assessment to identify both direct and transitive dependency risks. Crew AI agents collectively clone repositories, extract dependencies, analyse their version history, detect potential vulnerabilities, and predict the code-level impact of future updates. By offering actionable insights and automated risk predictions, the framework empowers developers to proactively remediate issues and optimize refactoring efforts before vulnerabilities escalate. This work demonstrates how intelligent agent-based automation can significantly enhance the accuracy, efficiency, and reliability of modern SCA practices.},
        keywords = {Software Composition Analysis, Crew AI, Dependency Management, GitHub Integration, Code Impact Prediction, Vulnerability Detection.},
        month = {December},
        }