# CRAB Webapp (Code Review Automation Benchmark) A research-driven platform for evaluating deep learning models on automated code review tasks. CRAB provides two core services: - **Dataset download**: Obtain high-quality, curated Java code review datasets for **comment generation** and **code refinement** tasks. - **Result evaluation**: Upload model-generated predictions to receive standardized evaluation metrics via a REST+WebSocket API. ## Features - **Static Frontend**: Vanilla HTML/CSS/JS interface—no build toolchain required. - **Dataset Delivery**: ZIP archives of JSON files, with optional full repo context. - **Submission Queue**: Server-managed job queue with configurable parallelism (via `MAX_WORKERS`). - **Real‑time Feedback**: Progress updates over WebSockets (using Flask-SocketIO). - **Robust Data Processing**: Utilities for parsing, validating, and evaluating submissions in `src/utils`. ## Prerequisites - **Python 3.8+** - *(Optional)* Docker daemon if you wish to execute the code refinement evaluation ## Installation & Setup 1. **Clone** the repository: ```bash git clone https://github.com/karma-riuk/crab-webapp.git cd crab-webapp ``` 1. *(Optional)* **Create Python Environement** ```bash python -m venv .venv source .venv/bin/activate ``` 1. **Install** Python dependencies: ```bash pip install -r requirements.txt ``` ### Environment Variables Defaults are set in `src/utils/env_defaults.py` (port 45003, `data/` path, etc.) and described in the comments of `.env.example`. To override: ```bash cp .env.example .env # Edit .env to adjust: # PORT=..., MAX_WORKERS=..., DATA_PATH=..., RESULTS_DIR=... ``` ## Running the Application From the project root: ```bash python src/server.py ``` - The Flask app serves static files from `public/` at `/` and mounts API routes under `/datasets` and `/answers` via Blueprints. - By default, open your browser to **[http://localhost:45003/](http://localhost:45003/)**. - If you want to try it out, you can go on **[http://gym.si.usi.ch:45003](http://gym.si.usi.ch:45003)** (you must be connected to USI network to access it). ## API Endpoints | Method | Route | Description | | ------ | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------- | | GET | `/datasets/download/` | Download ZIP of `comment_generation` or `code_refinement` (use `?withContext=true` for full repo).| | POST | `/answers/submit/comment` | Submit comment-generation JSON. | | POST | `/answers/submit/refinement` | Submit code-refinement JSON. | | GET | `/answers/status/` | Poll status or results (may include `X-Socket-Id` for notifications). | ## Project Structure ``` ├── data/ # Dataset files: dataset.json, archives, etc. ├── public/ # Static frontend │ ├── css/style.css # Styles │ ├── img/crab.png # Icon │ ├── index.html # UI with modals, schema docs │ └── js/ # Frontend scripts │ ├── index.js # UI logic, fetch & WebSocket handlers │ ├── modal.js # Modal dialogs │ └── sorttable.js # Table sorting ├── src/ # Backend source │ ├── server.py # App entry: Flask + SocketIO │ ├── routes/ # Blueprints │ │ ├── index.py # Root & health-check │ │ ├── datasets.py # File downloads │ │ └── answers.py # Submission & status endpoints │ └── utils/ # Core logic & helpers │ ├── env_defaults.py # Default ENV vars │ ├── dataset.py # Load/validate dataset JSON │ ├── process_data.py # Evaluation functions │ ├── observer.py # WebSocket observer & queue cleanup │ ├── queue_manager.py # Concurrency control │ └── build_handlers.py # Build/test wrappers ├── requirements.txt # Python libs: Flask, SocketIO, dotenv, etc. ├── TODO.md # Next steps and backlog └── .env.example # Template for environment variables ``` ## Contributing 1. **Issue Tracker**: Please file issues for bugs or feature requests. 1. **Pull Requests**: Fork, create a topic branch, and submit a PR. Please include tests or validations where applicable. ## Acknowledgements - Developed as part of a Master's thesis at Università della Svizzera Italiana.