Machine Learning-Powered Identification of Source Code Vulnerabilities

Abstract

These methods for identifying source code defects face limitations, including false positives and negatives, resource demands, and integration issues in modern software projects. This article explores cutting-edge research in using ML to enhance source code security. As ML gains traction in bug prediction, numerous studies investigate its potential. This research contributes to the growing interest in applying ML to source code, addressing the need for more efficient, accurate, and scalable defect detection methods. By leveraging ML techniques, software development processes can become more robust, reducing vulnerabilities and enhancing overall code quality.