Research progress on detection of software vulnerabilities based on code similarity

LI Qinghao; LIU Chunling; TANG Yonghe; DONG Weiyu

doi:10.11959/j.issn.2096-109x.2025065

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Research progress on detection of software vulnerabilities based on code similarity

Comprehensive Reviews | 更新时间：2026-01-28

- Research progress on detection of software vulnerabilities based on code similarity
- Chinese Journal of Network and Information Security Vol. 11, Issue 6, Pages: 36-62(2025)
- 作者机构：
  
  信息工程大学，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The Key R&D Special Program of Henan Province(221111210300)
- DOI：10.11959/j.issn.2096-109x.2025065
  CLC： TP393
- Received：17 March 2025，
  
  Revised：2025-05-17，
  
  Published：25 December 2025
- 稿件说明：
移动端阅览
李晴浩, 刘春玲, 唐永鹤, 等. 基于代码相似性的软件漏洞检测研究进展[J]. 网络与信息安全学报, 2025, 11(6): 36-62.

LI Qinghao, LIU Chunling, TANG Yonghe, et al. Research progress on detection of software vulnerabilities based on code similarity[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 36-62.
李晴浩, 刘春玲, 唐永鹤, 等. 基于代码相似性的软件漏洞检测研究进展[J]. 网络与信息安全学报, 2025, 11(6): 36-62. DOI： 10.11959/j.issn.2096-109x.2025065.

LI Qinghao, LIU Chunling, TANG Yonghe, et al. Research progress on detection of software vulnerabilities based on code similarity[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 36-62. DOI： 10.11959/j.issn.2096-109x.2025065.

摘要

软件漏洞是长期困扰网络与信息安全领域的一类关键威胁，而漏洞检测是缓解此类威胁的首要技术手段。基于代码相似性的漏洞检测具有可规模化、低成本的优势；特别是与图神经网络、循环神经网络等人工智能技术结合后，其准确性与适用性进一步提升，正成为该领域的研究热点。系统梳理了近7年来基于代码相似性的软件漏洞检测进展：从数据集角度，剖析了漏洞样本数据集的类别、粒度、规模及其平衡性；从代码预处理角度，总结了代码清理与标准化、反汇编与中间语言表示等两类方法；从代码表征角度，归纳了基于代码片段、哈希、树、图、指标及混合表征6种方案的检测机制、优缺点与适用场景，并在统一评测指标下对比其检测性能与开销；进一步提炼了所用机器学习技术的类别与特点。最后，总结了该研究面临的挑战，并展望了未来的研究方向。

Abstract

Software vulnerabilities have long been a key threat to the field of network and information security

and vulnerability detection is the primary technical means to mitigate such threats. Vulnerability detection based on code similarity offers the advantages of scalability and low cost

particularly when combined with artificial intelligence technologies such as graph neural networks and recurrent neural networks

further enhancing its accuracy and applicability

making it a research hotspot in this field. This paper systematically reviews the progress of software vulnerability detection based on code similarity in the past seven years: from the perspective of datasets

it analyzes the categories

granularity

scale

and balance of vulnerability sample datasets; from the perspective of code preprocessing

it summarizes two types of methods: code cleaning and standardization

and disassembly and intermediate language representation; from the perspective of code representation

it categorizes six schemes of detection mechanisms

advantages and disadvantages

and applicable scenarios based on code snippets

hashes

trees

graphs

metrics

and hybrid representations

and compares their detection performance and overhead under unified evaluation metrics. Furthermore

it distills the categories and characteristics of the machine learning techniques employed. Finally

it summarizes the challenges faced by this research and looks forward to future research directions.

关键词

Keywords

references

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