This is a demo program of PatchRNN, which identifies security patches using recurrent neural networks. The code changes in patches are processed using a twin RNN network, while the commit message is processed with a TextRNN network. Afterward, the information from the commit message and the code revision is fused in order to get the final prediction using a multi-layer perceptron.

More details about the PatchRNN can be found in the paper "PatchRNN: A Deep Learning-Based System for Security Patch Identification", to appear in the MILCOM 2021, San Diego, USA, November 29–December 2, 2021.

To cite our work, please use

@INPROCEEDINGS{PatchRNN2021Wang,
  author={Wang, Xinda and Wang, Shu and Feng, Pengbin and Sun, Kun and Jajodia, Sushil and Benchaaboun, Sanae and Geck, Frank},
  booktitle={MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)}, 
  title={PatchRNN: A Deep Learning-Based System for Security Patch Identification}, 
  year={2021},
  volume={},
  number={},
  pages={595-600},
  doi={10.1109/MILCOM52596.2021.9652940}}

This demo program is base on the PatchRNN Developer Edition (S2020.08.08-V4), developed by Shu Wang.

You can also visit PatchRNN official website: https://shuwang127.github.io/PatchRNN-demo/.

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

We use home directory to store the project folder.

cd ~

Install git tool.

sudo apt install git

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

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

Install pip tool for python3.

sudo apt install python3-pip

Install common dependencies.

pip3 install nltk==3.3
pip3 install natsort
pip3 install pandas
pip3 install sklearn

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

pip3 install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

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

cd ~/PatchRNN-demo/
python3 demo.py

There are 56 input test samples stored in ~/PatchRNN-demo/testdata/, the output results are saved in ~/PatchRNN-demo/results/results.txt.

cat results/results.txt

You can find the results.

.//testdata/nginx/release-1.19.0_release-1.19.1/0a683fdd.patch,1
.//testdata/nginx/release-1.19.0_release-1.19.1/1bbc37d3.patch,1
.//testdata/nginx/release-1.19.0_release-1.19.1/2afc050b.patch,0
.//testdata/nginx/release-1.19.0_release-1.19.1/2d4f04bb.patch,0
.//testdata/nginx/release-1.19.0_release-1.19.1/6bb43361.patch,1
...