title	SDLC-Copilot
emoji	🐳
colorFrom	purple
colorTo	gray
sdk	docker
app_port	7860

SDLC Copilot 🚀

AI-Powered Assistant for Automating the Software Development Lifecycle (SDLC)

📌 Overview

SDLC Copilot is an Agentic AI system designed to streamline and automate the Software Development Lifecycle (SDLC). From requirement gathering to deployment and maintenance, SDLC Copilot leverages AI to optimize development workflows, reduce manual effort, and ensure software quality.

Whether you're a solo developer, a startup, or an enterprise, SDLC Copilot acts as your AI-driven assistant to accelerate development, enforce best practices, and enhance collaboration across teams.

✨ Features

🔹 Requirement Analysis & User Story Generation

• AI-driven requirement analysis from user inputs.
• Automatic user story generation with structured acceptance criteria.
• Iterative refinement through feedback loops.

🔹 Design & Documentation

• AI-assisted design document creation (Functional & Technical).
• Auto-generated architecture diagrams and API documentation.
• Smart feedback analysis from design reviews.

🔹 Intelligent Code Generation & Review

• AI-assisted code generation based on design specifications.
• Integrated code review bot for best practices, security, and optimization.
• Automated code refactoring and bug detection.

🔹 Security & Compliance Checks

• AI-driven security vulnerability detection in the codebase.
• Compliance checks for OWASP, GDPR, HIPAA, ISO27001, and more.
• Automated code fixes based on security scans.

🔹 Automated Testing & Quality Assurance

• AI-generated unit, integration, and regression test cases.
• Test case review and refinement with automated feedback loops.
• Intelligent bug detection and self-healing test scripts.

🔹 Continuous Integration & Deployment (CI/CD)

• AI-powered DevOps pipeline automation for CI/CD workflows.
• Smart deployment strategies with rollback and monitoring features.
• Cloud-native integration with AWS, Azure, GCP, and Kubernetes.

🔹 Post-Deployment Monitoring & Maintenance

• AI-based real-time application monitoring for anomalies and crashes.
• Predictive maintenance using historical data and AI analytics.
• Automated patching and updates with zero downtime strategies.

🛠 Tech Stack

• AI/ML: OpenAI, LangChain, Hugging Face
• Backend: Python, FastAPI
• Frontend: React.js, Next.js
• CI/CD: GitHub Actions,, Docker
• LLMs : ChatGPT, Cluade, Deepseek

🚀 Getting Started

1️⃣ Prerequisites

• Docker and Docker Compose
  • Install Docker
  • Install Docker Compose
• Python 3.8+
  • Install Python
  • Install pip
• Node.js 16+ and npm
  • Install Node.js and npm

2️⃣ Installation

1. Clone the repository:

git clone https://github.com/shubhamprajapati7748/sdlc-copilot.git
cd sdlc-copilot

3️⃣ Local Development Setup

Backend Setup (FastAPI)

1. Create and activate virtual environment

   Using Anaconda:

   # Create a new conda environment
   conda create -p venv python==3.12 -y
   conda activate venv/

   OR Using Python venv (Alternative):

   # Create virtual environment
   python -m venv venv
   
   # Activate virtual environment
   # On macOS/Linux:
   source venv/bin/activate
   # On Windows:
   .\venv\Scripts\activate

2. Set up the backend:

   # Navigate to backend directory
   cd backend
   
   # Install dependencies
   pip install -r requirements.txt

3. Configure environment variables:

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env file with your configuration
   # Required all variables

4. Start the backend server:

   # Run the development server
   uvicorn app:app --reload --port 8000

   The backend API will be available at http://127.0.0.1:8000

   • API documentation: http://127.0.0.1:8000/docs

Frontend Setup (Vite + React)

1. Set up the frontend:

   # Navigate to frontend directory
   cd frontend
   
   # Install dependencies
   npm install
   # or
   yarn install

2. Configure environment variables:

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env file with your configuration
   # Required variables:
   VITE_BACKEND_URL="http://127.0.0.1:8000"

3. Start the development server:

   # Run the development server
   npm run dev
   # or
   yarn dev

   The frontend will be available at http://localhost:5173

5️⃣ Troubleshooting

Common issues and solutions:

1. Port conflicts:

   • If port 8000 is in use, modify the port in the backend command
   • If port 5173 is in use, Vite will automatically suggest an alternative port

2. Environment variables:

   • Ensure all required environment variables are set in both .env files
   • Restart the servers after modifying environment variables

3. Dependencies issues:

   • Delete node_modules and reinstall if frontend dependencies fail
   • Recreate virtual environment if backend dependencies fail

4. API connection:

   • Verify the VITE_BACKEND_URL in frontend .env matches your backend URL
   • Check if the backend server is running and accessible

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🤝 Contribution

Contributions to the SDLC-Copilot are welcome! If you have suggestions, enhancements, or bug fixes, please follow the steps below:

1. Fork the project.
2. Create your feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

🔏 License

Distributed under the MIT License. See LICENSE.txt for more information.

📧 Contact & Support

• Shubham Prajapati - shubhamprajapati7748@gmail.com

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