mlsanityData to 7 Oct 2026

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

arXiv 1909.03496 ↗First posted 8 Sep 2019cs.SE cs.CR cs.LGcited by 266

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Cited by 23 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the source code to facilitate the fix. However, it is a challenging and tedious process, and also requires specialized security expertise. Inspired by the work on manually-defined patterns of vulnerabilities from various code representation graphs and the recent advance on graph neural networks, we propose Devign, a general graph neural network based model for graph-level classification through learning on a rich set of code semantic representations. It includes a novel Conv module to efficiently extract useful features in the learned rich node representations for graph-level classification. The model is trained over manually labeled datasets built on 4 diversified large-scale open-source C projects that incorporate high complexity and variety of real source code instead of synthesis code used in previous works. The results of the extensive evaluation on the datasets demonstrate that Devign outperforms the state of the arts significantly with an average of 10.51% higher accuracy and 8.68% F1 score, increases averagely 4.66% accuracy and 6.37% F1 by the Conv module.

Key facts. Cited by 266 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 26; 2022: 38; 2023: 45; 2024: 55; 2025: 59; 2026: 42.

266 papers.

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  1. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  2. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 732
  3. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  4. 2021Unified Pre-training for Program Understanding and Generationcited by 361
  5. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  6. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  7. 2024Vulnerability Detection with Code Language Models: How Far Are We?cited by 133
  8. 2023DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detectioncited by 98
  9. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  10. 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
  11. 2023Large Language Models for Test-Free Fault Localizationcited by 67
  12. 2024Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAGcited by 67
38 more
  1. 2022CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Contextcited by 64
  2. 2021SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representationcited by 63
  3. 2021CoTexT: Multi-task Learning with Code-Text Transformercited by 61
  4. 2024LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoningcited by 57
  5. 2022VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detectioncited by 51
  6. 2023Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generationcited by 49
  7. 2021D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysiscited by 48
  8. 2022CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Modelscited by 48
  9. 2023xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrievalcited by 46
  10. 2022An Empirical Study of Deep Learning Models for Vulnerability Detectioncited by 44
  11. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 44
  12. 2023A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verificationcited by 42
  13. 2022No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligencecited by 41
  14. 2022Transformer-Based Language Models for Software Vulnerability Detectioncited by 39
  15. 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
  16. 2022Fault-Aware Neural Code Rankerscited by 33
  17. 2021ReGVD: Revisiting Graph Neural Networks for Vulnerability Detectioncited by 32
  18. 2023The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verificationcited by 32
  19. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  20. 2024AST-T5: Structure-Aware Pretraining for Code Generation and Understandingcited by 32
  21. 2024An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detectioncited by 32
  22. 2023Uncovering the Limits of Machine Learning for Automatic Vulnerability Detectioncited by 30
  23. 2024Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuningcited by 30
  24. 2025Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositoriescited by 29
  25. 2025EuroBERT: Scaling Multilingual Encoders for European Languagescited by 28
  26. 2023How Far Have We Gone in Vulnerability Detection Using Large Language Modelscited by 27
  27. 2024Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detectioncited by 27
  28. 2021How could Neural Networks understand Programs?cited by 26
  29. 2021On Multi-Modal Learning of Editing Source Codecited by 25
  30. 2024Astraios: Parameter-Efficient Instruction Tuning Code Large Language Modelscited by 23
  31. 2024NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctnesscited by 23
  32. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  33. 2025Frontier AI's Impact on the Cybersecurity Landscapecited by 21
  34. 2021Generating Adversarial Computer Programs using Optimized Obfuscationscited by 20
  35. 2021VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatementscited by 20
  36. 2022Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Codecited by 20
  37. 2023Multi-target Backdoor Attacks for Code Pre-trained Modelscited by 19
  38. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 18

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