计算机应用与软件2026，Vol.43Issue(1)：50-58,74,10.DOI:10.3969/j.issn.1000-386x.2026.01.007
基于半监督聚类的安全补丁分类方法
SECURITY PATCH CLASSIFICATION BASED ON SEMI-SUPERVISED CLUSTERING
摘要
Abstract
Due to the lack of labeled samples and the poor feature representation,current approaches do not perform well in classifying security patches for open source software(OSS).To alleviate these problems,we propose a semi-supervised clustering-based security patch classification method.The features were extracted from commit messages,and encoded with contrastive learning to enhance the difference between types of samples.Based on feature similarity,semi-supervised clustering was performed to form clusters with more sufficient data distributions.The method classified samples by measuring their similarity to each cluster.Experimental results show that the proposed method can effectively classify security patches with higher accuracy than existing state-of-the-art methods.关键词
开源软件/安全补丁分类/半监督聚类/对比学习Key words
Open source software/Security patch classification/Semi-supervised clustering/Contrastive learning分类
信息技术与安全科学引用本文复制引用
曹家俊,谈心,张源..基于半监督聚类的安全补丁分类方法[J].计算机应用与软件,2026,43(1):50-58,74,10.基金项目
国家自然科学基金项目(62172105) （62172105）
上海市青年科技启明星计划项目(21QA1400700) （21QA1400700）
上海市基础研究特区计划项目(21TQ1400100:21TQ012). （21TQ1400100:21TQ012）
