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arXiv · 2601.04085

CSSG: Measuring Code Similarity with Semantic Graphs

Yiyang Lu·Jingwen Xu·Changze Lv·Zisu Huang·Zhengkang Guo·Zhengyuan Wang·Muzhao Tian·Xuanjing Huang·Xiaoqing Zheng
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Abstract

Existing code similarity metrics, such as BLEU, CodeBLEU, and TSED, largely rely on surface-level string overlap or abstract syntax tree structures, and often fail to capture deeper semantic relationships between programs.We propose CSSG (Code Similarity using Semantic Graphs), a novel metric that leverages program dependence graphs to explicitly model control dependencies and variable interactions, providing a semantics-aware representation of code.Experiments on the CodeContests+ dataset show that CSSG consistently outperforms existing metrics in distinguishing more similar code from less similar code under both monolingual and cross-lingual settings, demonstrating that dependency-aware graph representations offer a more effective alternative to surface-level or syntax-based similarity measures.

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Yiyang Lu, Jingwen Xu, Changze Lv, Zisu Huang, Zhengkang Guo, Zhengyuan Wang, Muzhao Tian, Xuanjing Huang, Xiaoqing Zheng. 2026-01-14. CSSG: Measuring Code Similarity with Semantic Graphs. https://arxiv.org/abs/2601.04085

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