mlsanityData to 7 Oct 2026

A Survey on Data-driven Software Vulnerability Assessment and Prioritization

arXiv 2107.08364 ↗First posted 18 Jul 2021cs.SE cs.AI cs.CRcited by 18

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Abstract (arXiv)

Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation plans based on various SV characteristics. The surges in SV data sources and data-driven techniques such as Machine Learning and Deep Learning have taken SV assessment and prioritization to the next level. Our survey provides a taxonomy of the past research efforts and highlights the best practices for data-driven SV assessment and prioritization. We also discuss the current limitations and propose potential solutions to address such issues.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 50 of its 217 references that are in the map. First posted on arXiv 18 Jul 2021.

Builds on

50 of 217 references.

References · 50

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38 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 7; 2025: 4.

18 papers.

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  1. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  2. 2021DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learningcited by 11
  3. 2024Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Studycited by 9
  4. 2025LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurationscited by 9
  5. 2024Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPTcited by 8
  6. 2021Automated Security Assessment for the Internet of Thingscited by 6
  7. 2024Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?cited by 6
  8. 2024Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++cited by 6
  9. 2024Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?cited by 6
  10. 2024EvalSVA: Multi-Agent Evaluators for Next-Gen Software Vulnerability Assessmentcited by 6
  11. 2022On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Modelscited by 5
  12. 2023XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detectioncited by 4
6 more
  1. 2024EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detectioncited by 2
  2. 2022Common Vulnerability Scoring System Prediction based on Open Source Intelligence Information Sourcescited by 1
  3. 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?cited by 1
  4. 2022Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimizationcited by 0
  5. 2025Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimizationcited by 0
  6. 2025Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspectivecited by 0