Abstract
Type-4 clones refer to a pair of code snippets with similar semantics but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce non-negligible noise into the detection models. To overcome these limitations, we design a novel semantic graph-based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate representation to represent the code semantic more precisely compared to the representations based on lexical and syntactic analysis. To accommodate the characteristics of Type-4 clones, a semantic graph is constructed focusing on the operators and API calls instead of all tokens. Then, SEED generates the feature vectors by using the graph match network and performs clone detection based on the similarity among the vectors. Extensive experiments show that our approach significantly outperforms two baseline approaches over two public datasets and one customized dataset. Especially, SEED outperforms other baseline methods by an average of 25.2% in the form of F1-Score. Our experiments demonstrate that SEED can reach state-of-the-art and be useful for Type-4 clone detection in practice.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their insightful comments. This work was substantially supported by National Natural Science Foundation of China (No. 61872373 and 61872375).
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Xue, Z., Jiang, Z., Huang, C., Xu, R., Huang, X., Hu, L. (2022). SEED: Semantic Graph Based Deep Detection for Type-4 Clone. In: Perrouin, G., Moha, N., Seriai, AD. (eds) Reuse and Software Quality. ICSR 2022. Lecture Notes in Computer Science, vol 13297. Springer, Cham. https://doi.org/10.1007/978-3-031-08129-3_8
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