基于增强控制流图与孪生网络架构的代码克隆检测方法

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关键词：控制流图；孪生网络架构；代码表征；语义相似性；克隆检测中图分类号：TP311 文献标志码：A 文章编号：1001-3695（2025）07-028-2132-09doi：10.19734/j. issn.1001-3695.2024.11.0441

Abstract：Toaddresstheissues of mising contextual information and weak semantic learning capabilities inexisting code clone detection methods，this paper proposedamethod basedonanenhancedcontrolflowgraph（ECFG）and twin network architecture.Firstly，itdesignedECFG，whichembeddedcross-nodecorelationedges tostrenghencontextualawareness. Then，itintroducedCGSMN，asemanticmatching modelbasedontwinnetworks.Thismodelintegratedamulti-headatntion mechanism to extractkeyinformationfromthenodes，thenimprovedtherelational graphatentionnetwork tocapture nternode associationsand generate graph feature vectors.Finall，it explored thesemanticrelationships between thesefeature vectorsandcomputedthesemanticsimilarity.Empirical evaluationwasconductedon tworepresentative datasets.Theresults show that，compared to methods such as ASTNN，FA-AST，and DHAST，the F1 -score on the BigCloneBench dataset improves by0.5 to15.5percentage points，andby1.5to16.5percentage pointsonthe GogleCode Jamdataset，demonstrating the effectiveness of the proposed method for semantic clone detection.

Key words：controlflow graph；siamese neural network；coderepresentation；codesemantic similarity；codeclonedetection

0 引言

近年来，开源文化的蓬勃发展催生了一系列以协作共享为理念的开发者社群，对软件开发模式产生颠覆性改变[1]，软件代码的复制、粘贴和修改等克隆模式成为一种普遍且高效的实践。（剩余23693字）