@inproceedings{rahaman-ive-2024-source,
    title = "Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection",
    author = "Rahaman, Mohammed  and
      Ive, Julia",
    editor = "Cao, Yang (Trista)  and
      Papadimitriou, Isabel  and
      Ovalle, Anaelia  and
      Zampieri, Marcos  and
      Ferraro, Francis  and
      Swayamdipta, Swabha",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.naacl-srw.20/",
    doi = "10.18653/v1/2024.naacl-srw.20",
    pages = "168--199",
    abstract = "Code clone detection is challenging, as sourcecode can be written in different languages, do-mains, and styles. In this paper, we arguethat source code is inherently a graph, not asequence, and that graph-based methods aremore suitable for code clone detection thansequence-based methods. We compare the per-formance of two state-of-the-art models: Code-BERT (Feng et al., 2020), a sequence-basedmodel, and CodeGraph (Yu et al., 2023), agraph-based model, on two benchmark data-sets: BCB (Svajlenko et al., 2014) and PoolC(PoolC, no date). We show that CodeGraphoutperforms CodeBERT on both data-sets, es-pecially on cross-lingual code clones. To thebest of our knowledge, this is the first work todemonstrate the cross-lingual code clone detec-tion showing superiority on graph-based meth-ods over sequence-based methods"
}