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Comparison of Code Similarity Analysis Performance of funcGNN and Siamese Network  

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)
Publication Information
Journal of the Semiconductor & Display Technology / v.20, no.3, 2021 , pp. 113-116 More about this Journal
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
Code Similarity; funcGNN; Siamese Network; Control Flow Graph; Adjacency Matrices;
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