# Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection

arXiv: https://arxiv.org/abs/2312.16488 · HTML: https://mlsanity.com/paper/2312.16488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning](https://mlsanity.com/paper/2301.09072/) (2023)
- [Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks](https://mlsanity.com/paper/2011.11228/) (2020)

## Abstract (arXiv)

Source code clone detection is the task of finding code fragments that have the same or similar functionality, but may differ in syntax or structure. This task is important for software maintenance, reuse, and quality assurance (Roy et al. 2009). However, code clone detection is challenging, as source code can be written in different languages, domains, and styles. In this paper, we argue that source code is inherently a graph, not a sequence, and that graph-based methods are more suitable for code clone detection than sequence-based methods. We compare the performance of two state-of-the-art models: CodeBERT (Feng et al. 2020), a sequence-based model, and CodeGraph (Yu et al. 2023), a graph-based model, on two benchmark data-sets: BCB (Svajlenko et al. 2014) and PoolC (PoolC no date). We show that CodeGraph outperforms CodeBERT on both data-sets, especially on cross-lingual code clones. To the best of our knowledge, this is the first work to demonstrate the superiority of graph-based methods over sequence-based methods on cross-lingual code clone detection.