DeepInfer: Deep Type Inference from Smart Contract Bytecode

The artifact can be found at https://github.com/sepine/DeepInfer

1. Environment Installation

Make sure the Anaconda has been installed in your computer. All the experiments are conducted on a Linux Server equipped with NVIDIA GeForce RTX 3090 24GB. Make sure your GPU environment has enough memory. CPU is not supported. CUDA120 and above is not supported.

(1) Run the following command

conda env create -f deepinfer.yml

conda activate deepinfer

Activate this environment and install the following libraries.

(1) torch==1.11.0+cu113

pip install torch-1.11.0+cu113-cp39-cp39-linux_x86_64.whl Please download the whl file from <https://download.pytorch.org/whl/torch/>

(2) torch-scatter==2.0.9 and torch-sparse==0.6.15

pip install torch_scatter-2.0.9-cp39-cp39-linux_x86_64.whl 
pip install torch_sparse-0.6.15-cp39-cp39-linux_x86_64.whl
Please download the whl file from <https://pytorch-geometric.com/whl/torch-1.11.0%2Bcu113.html>  

(3) torch-geometric==2.1.0.post1

pip install torch-geometric==2.1.0.post1

Note: Please update the CUDA version according to your actual version for pytorch.

2. Dataset

Please download the dataset from https://connectpolyu-my.sharepoint.com/:u:/g/personal/22037545r_connect_polyu_hk/Ecd6xOM9ttBBu6VF-vdYnhUBqhyobvvRKQ9CFghm_V6xzw?e=WHc8NY

Note: the size of the file is about 90GB. the size of the unzipped file is about 300GB. Please make sure your PC have enought disk space.

Unzip the dataset and put them in the same directory as the main files as follows:

├─cached
    ├─compiler0.5
        ├─param
        ├─return
    ├─compiler0.6
        ├─param
        ├─return
    ├─compiler0.7
        ├─param
        ├─return
    ├─compiler0.8
        ├─param
        ├─return
    ├─solidity
        ├─param
        ├─return
    ├─vyper
        ├─param
        ├─return
├─datasets
    ├─compiler0.5
        ├─param
        ├─return
    ├─compiler0.6
        ├─param
        ├─return
    ├─compiler0.7
        ├─param
        ├─return
    ├─compiler0.8
        ├─param
        ├─return
    ├─solidity
        ├─param
        ├─return
    ├─vyper
        ├─param
        ├─return
├─models
    ├─compiler0.5
        ├─param
        ├─return
    ├─compiler0.6
        ├─param
        ├─return
    ├─compiler0.7
        ├─param
        ├─return
    ├─compiler0.8
        ├─param
        ├─return
    ├─solidity
        ├─param
        ├─return
    ├─vyper
        ├─param
        ├─return
├─xxx.py

3. Run the models

Note: Please modify the number (i.e., batch size) according to your available GPU memory

Note: Please empty the 'results' folder before running the scripts (if available)

(1) Parameter Prediction for Solidity

bash run_solidity_param.sh 128

(2) Return Prediction for Solidity

bash run_solidity_return.sh 128

(3) Parameter Prediction for Vyper

bash run_vyper_param.sh 128

(4) Return Prediction for Vyper

bash run_vyper_return.sh 128

(5) Parameter Prediction for Different Compiler Versions

bash run_compiler0.5_param.sh 128

bash run_compiler0.6_param.sh 128

bash run_compiler0.7_param.sh 128

bash run_compiler0.8_param.sh 128

(6) Return Prediction for Different Compiler Versions

bash run_compiler0.5_return.sh 128

bash run_compiler0.6_return.sh 128

bash run_compiler0.7_return.sh 128

bash run_compiler0.8_return.sh 128