PatchGNN-demo

This is a demo program of PatchGNN, a graph neural network based model to detect security patches with their code property graphs (PatchCPGs).

How to Run PatchGNN-demo

1. Install OS

We use Ubuntu 20.04.2.0 LTS (Focal Fossa) desktop version.
Download Link: https://releases.ubuntu.com/20.04/ubuntu-20.04.2.0-desktop-amd64.iso

The virtualization software in our experiments is VirtualBox 5.2.24.
Download Link: https://www.virtualbox.org/wiki/Download_Old_Builds_5_2.
You can use other software like VMware Workstation.

System configurations:
RAM: 2GB
Disk: 25GB
CPU: 1 core in i7-7700HQ @ 2.8GHz

2. Download the source code from github

We use home directory to store the project folder.

cd ~

Install git tool.

sudo apt install git

Download PatchGNN-demo project to local disk. (You may need to enter your github account/password.)

git clone https://github.com/shuwang127/PatchGNN-demo

3. Install the dependencies.

(1) Install pip tool for python3.

sudo apt install python3-pip

(2) Install common dependencies.

pip3 install numpy
pip3 install pandas

(3) Install CPU-version PyTorch. Official website: https://pytorch.org/.

pip3 install torch==1.10.0+cpu torchvision==0.11.1+cpu torchaudio==0.10.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html

(4) Install clang tool.

pip3 install clang==6.0.0.2

Configurate the clang environment.

sudo apt install clang
cd /usr/lib/x86_64-linux-gnu/
sudo ln -s libclang-*.so.1 libclang.so

(5) Install Torch-Geometric. Official website: https://pytorch-geometric.readthedocs.io/en/latest/.

pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html

4. Run the demo program.

cd ~/PatchGNN-demo/
python3 test.py

There are 6 input test samples stored in ~/PatchGNN-demo/testdata/, the output results are saved in ~/PatchGNN-demo/logs/test_results.txt.

cat logs/test_results.txt

You can find the results.

filename,prediction
./testdata/02cca547\out_slim_ninf_noast_n1_w.log,0
./testdata/661e4086\out_slim_ninf_noast_n1_w.log,1
./testdata/9a3ec202\out_slim_ninf_noast_n1_w.log,1
./testdata/dac90a4b\out_slim_ninf_noast_n1_w.log,1
./testdata/e3797a66\out_slim_ninf_noast_n1_w.log,0
./testdata/fc785b12\out_slim_ninf_noast_n1_w.log,0