Literature review on vulnerability detection using NLP technology

Abstract

Vulnerability detection has always been the most important task in the field of software security. With the development of technology, in the face of massive source code, automated analysis and detection of vulnerabilities has become a current research hotspot. For special text files such as source code, using some of the hottest NLP technologies to build models and realize the automatic analysis and detection of source code has become one of the most anticipated studies in the field of vulnerability detection. This article does a brief survey of some recent new documents and technologies, such as CodeBERT, and summarizes the previous technologies.

References in corpus (33)

• Deep Learning in Neural Networks: An Overview
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Transformers in Vision: A Survey
• Language Models are Few-Shot Learners
• XLNet: Generalized Autoregressive Pretraining for Language Understanding
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• Convolutional Neural Networks for Sentence Classification
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• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
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• Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
• A Survey of Domain Adaptation for Neural Machine Translation
• BERT: A Review of Applications in Natural Language Processing and Understanding
• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
• A Survey of Word Embeddings Evaluation Methods
• A Survey of Deep Learning Techniques for Neural Machine Translation
• A systematic review of fuzzing based on machine learning techniques
• A Survey of Deep Learning Methods for Relation Extraction
• A Survey on Transfer Learning in Natural Language Processing
• Unified Pre-training for Program Understanding and Generation
• Heterogeneous Graph Neural Networks for Extractive Document Summarization
• Comparative evaluation of CNN architectures for Image Caption Generation
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