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LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward by Islam, Nafis Tanveer; Khoury, Joseph; Seong, Andrew; Karkevandi, Mohammad Bahrami; Parra, Gonzalo De La Torre; Bou-Harb, Elias; Najafirad, Peyman is a scholarly article available to read on EtoBox.

What is LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward about?

In software development, the predominant emphasis on functionality often supersedes security concerns, a trend gaining momentum with AI-driven automation tools like GitHub Copilot. These tools significantly improve developers' efficiency in functional code development. Nevertheless, it remains a notable concern that such tools are also responsible for creating insecure code, predominantly because of pre-training on publicly available repositories with vulnerable code. Moreover, developers are called the "weakest link in the chain" since they have very minimal knowledge of code security. Although existing solutions provide a reasonable solution to vulnerable code, they must adequately describe and educate the developers on code security to ensure that the security issues are not repeated. Therefore we introduce a multipurpose code vulnerability analysis system \texttt{SecRepair}, powered by a large language model, CodeGen2 assisting the developer in identifying and generating fixed code along with a complete description of the vulnerability with a code comment. Our innovative methodology uses a reinforcement learning paradigm to generate code comments augmented by a semantic reward

Author

Islam, Nafis Tanveer; Khoury, Joseph; Seong, Andrew; Karkevandi, Mohammad Bahrami; Parra, Gonzalo De La Torre; Bou-Harb, Elias; Najafirad, Peyman

Published

2024

Language

EN