A PRISMA-guided review of 21 papers shows RL work on C/C++ vulnerabilities focuses on fuzzing rather than detection or localization, proposes a taxonomy, and flags the lack of CFG-based state representations for vulnerable node identification.
In: 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)
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Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis
A PRISMA-guided review of 21 papers shows RL work on C/C++ vulnerabilities focuses on fuzzing rather than detection or localization, proposes a taxonomy, and flags the lack of CFG-based state representations for vulnerable node identification.