Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Comparison of Code Similarity Analysis Performance of funcGNN and Siamese Network

funcGNN과 Siamese Network의 코드 유사성 분석 성능비교

  • Choi, Dong-Bin (Dept. of Computer Science, Dankook University) ;
  • Jo, In-su (Dept. of Computer Science, Dankook University) ;
  • Park, Young B. (Dept. of Software Science, Dankook University)
  • 최동빈 (단국대학교 컴퓨터학과) ;
  • 조인수 (단국대학교 컴퓨터학과) ;
  • 박용범 (단국대학교 소프트웨어학과)
  • Received : 2021.09.02
  • Accepted : 2021.09.16
  • Published : 2021.09.30
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Abstract

As artificial intelligence technologies, including deep learning, develop, these technologies are being introduced to code similarity analysis. In the traditional analysis method of calculating the graph edit distance (GED) after converting the source code into a control flow graph (CFG), there are studies that calculate the GED through a trained graph neural network (GNN) with the converted CFG, Methods for analyzing code similarity through CNN by imaging CFG are also being studied. In this paper, to determine which approach will be effective and efficient in researching code similarity analysis methods using artificial intelligence in the future, code similarity is measured through funcGNN, which measures code similarity using GNN, and Siamese Network, which is an image similarity analysis model. The accuracy was compared and analyzed. As a result of the analysis, the error rate (0.0458) of the Siamese network was bigger than that of the funcGNN (0.0362).

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT육성지원사업의 연구결과로 수행되었음(IITP-2020-2017-0-01628).

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