Contributing

Thank you for your interest in contributing to AiDotNet! This guide will help you get started.

Getting Started

Prerequisites

• .NET 8.0 SDK or later
• Git
• A code editor (VS Code, Visual Studio, JetBrains Rider)
• Optional: CUDA Toolkit for GPU development

Fork and Clone

1. Fork the repository on GitHub
2. Clone your fork:

git clone https://github.com/YOUR_USERNAME/AiDotNet.git
cd AiDotNet

3. Add the upstream remote:

git remote add upstream https://github.com/ooples/AiDotNet.git

Build the Project

dotnet build

Run Tests

dotnet test

Development Workflow

Create a Branch

Always create a branch for your changes:

git checkout -b feature/your-feature-name

Use these prefixes:

• feature/ - New features
• fix/ - Bug fixes
• docs/ - Documentation
• refactor/ - Code refactoring
• perf/ - Performance improvements
• test/ - Test additions/changes

Make Changes

1. Write clean, readable code
2. Follow the existing code style
3. Add XML documentation for public APIs
4. Include unit tests for new functionality

Commit Guidelines

Write clear commit messages:

type(scope): Short description

Longer description if needed. Explain what and why,
not how.

Co-Authored-By: Your Name <your@email.com>

Types: feat, fix, docs, style, refactor, perf, test, chore

Submit a Pull Request

1. Push your branch:

git push origin feature/your-feature-name

2. Open a PR on GitHub
3. Fill out the PR template
4. Wait for review

Code Style

C# Guidelines

• Use modern C# features (pattern matching, records, etc.)
• Prefer expression-bodied members for simple methods
• Use nullable reference types
• Follow Microsoft naming conventions

// Good
public class NeuralNetwork<T> where T : struct, INumber<T>
{
    private readonly ILayer<T>[] _layers;

    public int LayerCount => _layers.Length;

    public Tensor<T> Forward(Tensor<T> input)
    {
        var current = input;
        foreach (var layer in _layers)
        {
            current = layer.Forward(current);
        }
        return current;
    }
}

Documentation

• Add XML documentation for all public types and members
• Include code examples in documentation
• Document exceptions that can be thrown

/// <summary>
/// Applies the forward pass through all layers.
/// </summary>
/// <param name="input">The input tensor.</param>
/// <returns>The output tensor after passing through all layers.</returns>
/// <exception cref="ArgumentNullException">Thrown when input is null.</exception>
public Tensor<T> Forward(Tensor<T> input)

Testing

Writing Tests

• Place tests in the tests/ directory
• Mirror the source structure
• Use descriptive test names

public class NeuralNetworkTests
{
    [Fact]
    public void Forward_WithValidInput_ReturnsExpectedShape()
    {
        // Arrange
        var network = new NeuralNetwork<float>(/* ... */);
        var input = Tensor<float>.Zeros(1, 10);

        // Act
        var output = network.Forward(input);

        // Assert
        Assert.Equal(new[] { 1, 5 }, output.Shape);
    }
}

Running Specific Tests

dotnet test

dotnet test --filter "NeuralNetworkTests"

dotnet test --logger "console;verbosity=detailed"

Areas to Contribute

Good First Issues

Look for issues labeled good first issue:

• Documentation improvements
• Bug fixes with clear reproduction steps
• Test coverage improvements
• Code style fixes

High-Impact Areas

• Neural Network Architectures: Add new model implementations
• Computer Vision: Object detection, segmentation models
• Audio Processing: Speech recognition, TTS improvements
• Optimizers: New optimization algorithms
• Documentation: Tutorials, examples, API docs

Performance

• SIMD optimizations for tensor operations
• GPU kernel improvements
• Memory allocation optimizations
• Benchmark improvements

PR Review Process

What to Expect

1. Automated Checks: CI runs tests and linting
2. Code Review: Maintainers review your code
3. Feedback: You may receive suggestions or requests
4. Approval: Once approved, your PR will be merged

Review Criteria

• Does it follow the code style?
• Are there adequate tests?
• Is the documentation updated?
• Does it maintain backward compatibility?
• Is the performance acceptable?

Communication

Where to Ask Questions

• GitHub Discussions: General questions and ideas
• GitHub Issues: Bug reports and feature requests
• PR Comments: Code-specific questions

Be Respectful

• Be kind and constructive
• Assume good intentions
• Help others learn

Recognition

Contributors are recognized in:

• Release notes for significant contributions
• The GitHub contributors page
• Special mentions for major features

Thank you for contributing to AiDotNet!