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Contributing to DeepScan Framework

Thank you for your interest in contributing to the DeepScan Framework! This document provides guidelines and instructions for contributing.

How to Contribute

Reporting Issues

If you find a bug or have a feature request, please open an issue on GitHub with:

  • A clear description of the problem or feature
  • Steps to reproduce (for bugs)
  • Expected vs actual behavior
  • Your environment (Python version, OS, etc.)

Contributing Code

  1. Fork the repository and create a new branch for your changes

    git checkout -b feature/your-feature-name

  2. Make your changes following the coding standards below

  3. Test your changes

    # Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
black deepscan/
flake8 deepscan/
mypy deepscan/

  4. Commit your changes with clear, descriptive commit messages

    git commit -m "Add feature: description of what you added"

  5. Push to your fork and open a Pull Request

Pull Request Guidelines

  • Provide a clear description of what your PR does
  • Reference any related issues
  • Ensure all tests pass
  • Update documentation if needed
  • Follow the existing code style

Code Style

Python Style

  • Follow PEP 8 style guidelines
  • Use black for code formatting (line length: 100)
  • Type hints are encouraged but not required for all functions
  • Use descriptive variable and function names

Project Structure

  • Models: Add new model runners in deepscan/models/
  • Evaluators: Add new evaluators in deepscan/evaluators/
  • Datasets: Add new dataset loaders in deepscan/datasets/
  • Summarizers: Add new summarizers in deepscan/summarizers/

Adding New Components

Adding a New Model Runner

  1. Create a new file in deepscan/models/ (e.g., my_model.py)
  2. Implement a class inheriting from BaseModelRunner
  3. Register the model in the model registry: 
    from deepscan.registry.model_registry import get_model_registry

@get_model_registry().register_model("my_model")
def create_my_model(**kwargs):
    # Your model creation logic
    return MyModelRunner(...)
  4. Import and register in deepscan/models/__init__.py

Adding a New Evaluator

  1. Create a new file in deepscan/evaluators/ (e.g., my_evaluator.py)
  2. Implement a class inheriting from BaseEvaluator
  3. Register the evaluator: 
    from deepscan.evaluators.registry import get_evaluator_registry

@get_evaluator_registry().register_evaluator("my_evaluator")
class MyEvaluator(BaseEvaluator):
    def evaluate(self, model, dataset, **kwargs):
        # Your evaluation logic
        return results
  4. Add to deepscan/evaluators/__init__.py if needed

Adding a New Dataset

  1. Create a new file in deepscan/datasets/ (e.g., my_dataset.py)
  2. Implement a dataset loader function
  3. Register the dataset: 
    from deepscan.registry.dataset_registry import get_dataset_registry

@get_dataset_registry().register_dataset("my_dataset")
def load_my_dataset(**kwargs):
    # Your dataset loading logic
    return dataset
  4. Add to deepscan/datasets/__init__.py if needed

Testing

  • Write tests for new features in the tests/ directory
  • Follow the existing test patterns
  • Ensure tests pass with pytest
  • Aim for good test coverage

Documentation

  • Update README.md if you add new features
  • Add docstrings to new functions and classes
  • Follow the existing documentation style
  • Update example configs if you change configuration schemas

Development Setup

# Clone the repository
git clone https://github.com/AI45Lab/DeepScan.git
cd DeepScan

# Install in development mode with all dependencies
pip install -e ".[dev,all]"

# Run tests
pytest

# Format code
black deepscan/

# Type checking
mypy deepscan/

Questions?

If you have questions about contributing, please open an issue or contact the maintainers.

Thank you for contributing to DeepScan Framework!