self-supervised learning of smart contract representation
Learning smart contract representations can greatly facilitate the development of smart contracts in many tasks such as bug 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, here, we propose SRCL, a novel, self-supervised approach for learning smart contract representations. Unlike existing supervised methods, which are tied on task-oriented 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 structure trees (ASTs). Then, two discriminators (local and global) are designed to guide the Transformer encoder to learn local and global semantic features of smart contracts.
This repository includes the source code for the paper ' Self-Supervised Learning of Smart Contract Representations'.
numpy==1.18.1
torch==1.6.0
tqdm==4.46.0
solidity_parser==0.0.7
treelib==1.6.1
sklearn==0.24.0
- train dataset could be downloaded from here
- bug detection
- clone detection clone detection dataset could be downloaded from here
- code clustering code clustering dataset could be downloaded from here
Download the dataset and specify the file location in config.py.
To train our model:
python main.py --train
To evaluate our model:
python main.py --eval taskname [detect/clone/cluster]
[1] Liebel, L., & Körner, M. (2018). Auxiliary tasks in multi-task learning. arXiv preprint arXiv:1805.06334.
[2] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.