I Know Who Clones Your Code: Interpretable Smart Contract Similarity Detection

Widespread reuse of open-source code in smart contract development boosts programming efficiency but significantly amplifies bug propagation across contracts, while dedicated methods for detecting similar smart contract functions remain very limited. Conventional abstract-syntax-tree (AST) based methods for smart contract similarity detection face challenges in handling intricate tree structures, which impedes detailed semantic comparison of code. Recent deep-learning based approaches tend to overlook code syntax and detection interpretability, resulting in suboptimal performance. To fill this research gap, we introduce <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartDetector</small>, a novel approach for computing similarity between smart contract functions, explainable at the fine-grained statement level. Technically, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartDetector</small> decomposes the AST of a smart contract function into a series of smaller statement trees, each reflecting a structural element of the source code. Then, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartDetector</small> uses a classifier to compute the similarity score of two functions by comparing each pair of their statement trees. To address the infinite hyperparameter space of the classifier, we mathematically derive a cosine-wise diffusion process to efficiently search optimal hyperparameters. Extensive experiments conducted on three large real-world datasets demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartDetector</small> outperforms current state-of-the-art methods by an average improvement of 14.01% in F1-score, achieving an overall average F1-score of 95.88%.