A Code Centric Evaluation of C/C++ Vulnerability Datasets for Deep Learning Based Vulnerability Detection Techniques
Ridhi Jain, Nicole Gervasoni, Mthandazo Ndhlovu, Sanjay Rawat
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.
Topics & Concepts
Computer scienceDeep learningCode (set theory)Vulnerability (computing)Task (project management)Artificial intelligenceComprehensionMachine learningDeep neural networksArtificial neural networkSimilarity (geometry)Data scienceNatural language processingProgramming languageComputer securityEngineeringImage (mathematics)Set (abstract data type)Systems engineeringSoftware Engineering ResearchAdvanced Malware Detection TechniquesSoftware Reliability and Analysis Research