KGCompass

KGCompass is a novel approach for repository-level software repair that accurately links code structure with repository metadata using a knowledge graph, enabling more precise bug localization and patch generation.

Paper link: https://arxiv.org/abs/2503.21710

Fully Containerized Workflow with GPU Support

This project uses Docker and Docker Compose to provide a fully reproducible environment. The setup includes: - A base image with CUDA and Python pre-installed. - A service for the Neo4j database with necessary plugins. - An application service with all Python dependencies and access to the host's GPU.

Prerequisites

1. NVIDIA GPU & Drivers: A compatible NVIDIA GPU with recent drivers installed on your host machine.
2. NVIDIA Container Toolkit: You must install this on your host to allow Docker to use the GPU. For Debian/Ubuntu-based systems, you can do so by running the following command block in your terminal: bash curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \ && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \ sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \ && sudo apt-get update \ && sudo apt-get install -y nvidia-container-toolkit \ && sudo nvidia-ctk runtime configure --runtime=docker \ && sudo systemctl restart docker
3. Docker & Docker Compose: This project uses Docker Compose V1.
   • Ensure Docker is installed on your system.
   • Install Docker Compose V1 (if not already present) by running: bash LATEST_COMPOSE_V1="1.29.2" sudo curl -L "https://github.com/docker/compose/releases/download/${LATEST_COMPOSE_V1}/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose sudo chmod +x /usr/local/bin/docker-compose
4. API Keys: Create a .env file in the project root by copying the example: bash cp .env.example .env Then, edit the .env file and fill in your GITHUB_TOKEN, and BAILIAN_API_KEY.

Step 1: Build and Start All Services

This single command will build the base CUDA image, the Neo4j image, and the final application image, then start all services in the background.

bash docker-compose up -d --build

Step 2: Run the Repair Pipeline

Execute the repair script inside the application container. The container will have access to the GPU.

```bash docker-compose exec app bash runrepair.sh <instanceid>

Example:

docker-compose exec app bash runrepair.sh astropy_astropy-12907 ```

Step 3: Stopping the Environment bash docker-compose down -v

Citation

If you use KGCompass in your research, please cite the following paper:

bibtex @article{yang2025enhancing, title={Enhancing Repository-Level Software Repair via Repository-Aware Knowledge Graphs}, author={Yang, Boyang and Tian, Haoye and Ren, Jiadong and Jin, Shunfu and Liu, Yang and Liu, Feng and Le, Bach}, journal={arXiv preprint arXiv:2503.21710}, year={2025} }