SmartEmbed is a web service tool for clone detection & bug detection for smart contracts. We have newly added the interface of using smartembed for estimating similarities between different smart contracts, please feel free to have a try! :) Any questions and feedback are very welcome.
Our full research paper: Checking smart contracts with structural code embeddings has been published on TSE (IEEE Transactions on Software Engineering), we describe the details for clone detection and bug detection in smart contracrs using SmartEmbed, for more details please refer to our research paper:
https://ieeexplore.ieee.org/document/8979435
https://arxiv.org/abs/2001.07125
SmartEmbed has been published as a tool demo by on ICSME-2019, for details of the implementation please refer to our paper:
https://arxiv.org/abs/1908.08615
Our work: When Deep Learning Meets Smart Contracts has been accepted by ASE-2020 Student Research Competition track, for more details please refer to our paper:
http://arxiv.org/abs/2008.04093
We have published our tool through the following url:
http://www.smartembed.tools/
There is a tutorial video introducing how to use SmartEmbed on Youtube:
https://youtu.be/o9ylyOpYFq8
Source data can be downloaded from:
https://drive.google.com/file/d/13iTTpt7gFd9wEW35C2fX4pVT7cVlHgxi/view?usp=sharing
Please cite our work if you found our work is helpful:
Checking smart contracts with structural code embeddings:
@article{gao2020checking,
title={Checking Smart Contracts with Structural Code Embedding},
author={Gao, Zhipeng and Jiang, Lingxiao and Xia, Xin and Lo, David and Grundy, John},
journal={IEEE Transactions on Software Engineering}, year={2020},
publisher={IEEE}
}
Smartembed: A tool for clone and bug detection in smart contracts through structural code embedding:
@inproceedings{gao2019smartembed,
title={Smartembed: A tool for clone and bug detection in smart contracts through structural code embedding},
author={Gao, Zhipeng and Jayasundara, Vinoj and Jiang, Lingxiao and Xia, Xin and Lo, David and Grundy, John},
booktitle={2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
pages={394--397},
year={2019},
organization={IEEE}
}
When Deep Learning Meets Smart Contracts
@article{gao2020deep,
title={When Deep Learning Meets Smart Contracts},
author={Gao, Zhipeng},
journal={arXiv preprint arXiv:2008.04093},
year={2020}
}
This folder contains the code for the SmartEmbed web tool. There are a few important subfolders and files as follows.
- templates - contains the frontend html files
- static - contains the css files and js scripts
- app[dot]py - main flask file, see below for usage.
- similarity[dot]py and smart_embed[dot]py - Contains the backend codes for clone detection.
- bug[dot]py and smart_bug[dot]py - Contains the backend codes for bug detection.
We have released the pre-trained model as described in the paper. You can use the following command to download our pretrained model:
pip install gdown
gdown https://drive.google.com/uc?id=1-LKJTZakqd8ntKzqVNtQZUgdZnFoYtpK
unzip Contract_Embedding.zip
cp -r Embedding/ SmartEmbed/contract_level/pip install gdown
gdown https://drive.google.com/uc?id=1lbaQVtZbNuEEjHIWVnwLqGvILxNWwtZW
unzip Contract_Model.zip
mv Model SmartEmbed/contract_level/pip install gdown
gdown https://drive.google.com/uc?id=18GiDgSwoRjPC25d2Vp15oi_xH2NivyXH
unzip Statement_Model.zip
mv Model SmartEmbed/statement_level/ - Install requirements.txt with
pip install -r requirements.txt. - Clone this project to your local
git clone https://github.com/beyondacm/SmartEmbed.git. - Please download the pretrained model with the aforementioned shell scripts.
- Change directory to
cd SmartEmbed/todo/, and Run the commandpython app.py. This will initialize the web tool atlocalhost:9000, as illustrated below. - Paste the smart contract on to the text area and hit Submit.
- Clone detection results will be displayed as follows.
- Bug detection results will be displayed as follows.
You can easily use smartembed tool to estimate the similarity between two smart contracts, the following code snippet gives an example:
from smartembed import SmartEmbed
se = SmartEmbed()
# read contract1 from file
contract1 = open('./todo/test.sol', 'r').read()
# get vector representation for contract1
vector1 = se.get_vector(contract1)
# read contract2 from file
contract2 = open('./todo/KOTH.sol', 'r').read()
# get vector representation for contract2
vector2 = se.get_vector(contract2)
# estimate similarity between contract1 and contract2
similarity = se.get_similarity(vector1, vector2)
print("similarity between c1 and c2:", similarity)zhipeng.gao@zju.edu.cn
vinojmh@smu.edu.sg
Discussions, suggestions and questions are welcome!