Self-Supervised Learning of Smart Contract Representations

Learning smart contract representations can greatly facilitate the development of smart contracts in many tasks such as bug detection and clone detection. Existing approaches for learning program representations are difficult to apply to smart contracts which have insufficient data and significant homogenization. To overcome these challenges, in this paper, we propose SRCL, a novel, self-supervised approach for learning smart contract representations. Unlike existing supervised methods, which are tied on task-specific data labels, SRCL leverages large-scale unlabeled data by self-supervised learning of both local and global information of smart contracts. It automatically extracts structural sequences from abstract syntax trees (ASTs). Then, two discriminators are designed to guide the Transformer encoder to learn local and global semantic features of smart contracts. We evaluate SRCL on a dataset of 75,006 smart contracts collected from Etherscan. Experimental results show that SRCL considerably outperforms the state-of-the-art code representation models on three downstream tasks.