A Code Centric Evaluation of C/C++ Vulnerability Datasets for Deep Learning Based Vulnerability Detection Techniques

Abstract

Recent years have witnessed tremendous progress in NLP-based code comprehension via deep neural networks (DNN) learning, especially Large Language Models (LLMs). While the original application of LLMs is focused on code generation, there have been attempts to extend the application to more specialized tasks, like code similarity, author attribution, code repairs, and so on. As data plays an important role in the success of any machine learning approach, researchers have also proposed several benchmarks which are coupled with a specific task at hand.