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Software vulnerabilities are security flaws, defects, or weaknesses in software architecture, design, or implementation. With the explosion of open source code available for analysis, there is a chance to learn about bug patterns that can lead to security vulnerabilities to assist in the discovery of vulnerabilities. Recent advances in deep learning in natural language processing, speech recognition, and image processing have demonstrated the great potential of neural models to understand natural language. This has encouraged researchers in the cybersecurity sector and software engineering to utilize deep learning to learn and understand vulnerable code patterns and semantics that indicate vulnerable code properties. In this paper, we review and analyze the recent state-of-the-art research adopting machine learning and deep learning techniques to detect software vulnerabilities, aiming to investigate how to …