mlsanityData to 7 Oct 2026

Literature review on vulnerability detection using NLP technology

arXiv 2104.11230 ↗First posted 23 Apr 2021cs.CR cs.AI cs.SEcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 63 of its 112 references that are in the map. First posted on arXiv 23 Apr 2021.

Builds on

63 of 112 references.

References · 63

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  7. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  8. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  9. 2018Deep contextualized word representationscited by 2,285
  10. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  11. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  12. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
38 more
  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  2. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  3. 2021Transformers in Vision: A Surveycited by 1,009
  4. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  5. 2015Gated Graph Sequence Neural Networkscited by 792
  6. 2020Efficient Transformers: A Surveycited by 784
  7. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  8. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 732
  9. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 600
  10. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 504
  11. 2021Unified Pre-training for Program Understanding and Generationcited by 361
  12. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
  13. 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 266
  14. 2018code2vec: Learning Distributed Representations of Codecited by 249
  15. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 177
  16. 2019Text Classification Algorithms: A Surveycited by 163
  17. 2017A Survey Of Cross-lingual Word Embedding Modelscited by 142
  18. 2020Neural Unsupervised Domain Adaptation in NLP---A Surveycited by 142
  19. 2020Deep Learning based Vulnerability Detection: Are We There Yet?cited by 142
  20. 2019An Attentive Survey of Attention Modelscited by 123
  21. 2019A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelscited by 117
  22. 2018Automated Vulnerability Detection in Source Code Using Deep Representation Learningcited by 114
  23. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  24. 2015A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classificationcited by 92
  25. 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
  26. 2019Multimodal Intelligence: Representation Learning, Information Fusion, and Applicationscited by 89
  27. 2017Comparative Study of CNN and RNN for Natural Language Processingcited by 81
  28. 2018A Survey of Domain Adaptation for Neural Machine Translationcited by 64
  29. 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
  30. 2018The Art, Science, and Engineering of Fuzzing: A Surveycited by 61
  31. 2020A Survey of Deep Learning Techniques for Neural Machine Translationcited by 50
  32. 2020A Survey on Contextual Embeddingscited by 48
  33. 2020A Survey on Machine Reading Comprehension Systemscited by 41
  34. 2020A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasetscited by 40
  35. 2014Effective Use of Word Order for Text Categorization with Convolutional Neural Networkscited by 39
  36. 2020VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detectorcited by 37
  37. 2020A Survey on Transfer Learning in Natural Language Processingcited by 33
  38. 2018An Introductory Survey on Attention Mechanisms in NLP Problemscited by 32

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Taskscited by 4
  2. 2025Enhancing Vulnerability Reports with Automated and Augmented Description Summarizationcited by 1