In software engineering, detecting vulnerabilities in code is a crucial task that ensures the security & reliability of software systems. If left unchecked, vulnerabilities can lead to significant security breaches, compromising the integrity of software and the data it handles. Over the years, the development of automated tools to detect these vulnerabilities has become increasingly important, particularly as software systems grow more complex and interconnected. A significant challenge in developing these automated tools is the lack of extensive and diverse datasets required to effectively train deep learning-based vulnerability detection (DLVD) models. Without sufficient data, these models struggle to accurately identify and generalize different types of vulnerabilities. This problem is compounded by the fact that existing methods for generating vulnerable code samples are often limited in scope, focusing on specific […]
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