AI-Native Software Development Lifecycle (SDLC)

A comprehensive guide to AI-powered software development, featuring detailed comparisons of AI coding assistants, implementation strategies, and enterprise best practices for the modern SDLC.

🚀 Features

• 45+ AI Coding Assistants Compared: Comprehensive analysis with 28 evaluation criteria
• Complete SDLC Methodology: End-to-end AI integration from requirements to production
• Enterprise Governance Framework: Security, compliance, and risk management
• ROI Calculator: Proven metrics showing 200-400% productivity improvements
• Implementation Roadmap: Step-by-step guidance for organizational adoption

📋 Table of Contents

• AI Tools Comparison
• SDLC Phases
• Governance & Security
• Implementation Guide

🛠️ Local Development

Prerequisites

• Ruby 3.0+
• Bundler
• Git

Setup

1. Clone the repository:

git clone https://github.com/CodySwannGt/ai-native-sdlc.git
cd ai-native-sdlc

2. Install dependencies:

bundle install

3. Run locally:

bundle exec jekyll serve

4. Open in browser:

http://localhost:4000/ai-native-sdlc/

Development Commands

# Build the site
bundle exec jekyll build

# Run with live reload
bundle exec jekyll serve --livereload

# Check for broken links
bundle exec htmlproofer ./_site

# Run linting
bundle exec rubocop

# Update dependencies
bundle update

📦 Project Structure

ai-native-sdlc/
├── _config.yml          # Jekyll configuration
├── _data/              # Data files (navigation, etc.)
├── _includes/          # Reusable components
├── _layouts/           # Page templates
├── _sass/              # Stylesheets
├── ai-tools/           # AI tools documentation
├── sdlc/               # SDLC phases documentation
├── governance/         # Governance documentation
├── implementation/     # Implementation guides
├── assets/             # Images, CSS, JS
└── index.md            # Homepage

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Content Guidelines

• Use clear, concise language
• Include practical examples
• Cite sources for statistics
• Keep tables and comparisons up-to-date
• Test all links and code examples

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• All AI tool vendors for their innovation
• The open-source community
• Early adopters and contributors

📞 Contact

• GitHub Issues: Report bugs or request features
• Discussions: Join the conversation

🔄 Recent Updates

• December 2024: Added Claude Code, OpenAI Codex, and Jules comparisons
• November 2024: Updated ROI metrics based on enterprise data
• October 2024: Enhanced governance framework

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