Skip to content

PROJECT_DOCUMENTATION.md

Latest commit

 

History

History
191 lines (145 loc) · 5.93 KB

PROJECT_DOCUMENTATION.md

File metadata and controls

191 lines (145 loc) · 5.93 KB

MediTestAI - Healthcare Test Case Generation System

Overview

MediTestAI is an AI-powered system that automates the generation of test cases for healthcare software applications. The system converts software requirements into compliant, structured test cases and integrates with enterprise toolchains.

Key Features

  1. AI-Powered Test Case Generation

    • Converts natural language and structured specifications into detailed test cases
    • Supports multiple input formats (PDF, Word, XML, Markdown)
    • Generates comprehensive test cases with all required elements

  2. Healthcare Compliance & Standards

    • Ensures compliance with FDA, ISO 13485, and GDPR standards
    • Provides compliance reports for generated test cases
    • Maintains traceability between requirements and test cases

  3. No-Code Interface

    • Intuitive UI for non-technical users
    • File upload and copy-paste input options
    • Visual test case management dashboard

  4. Test Case Management

    • View, copy, and export generated test cases
    • Modify existing test cases using natural language prompts
    • Import and manage existing test suites

Technology Stack

  • Frontend: Next.js 15 with React Server Components
  • AI Engine: Google Gemini through LangChain
  • UI Components: Tailwind CSS, Radix UI, Shadcn UI
  • State Management: React Context API
  • Backend: Next.js API routes with LangChain-based server actions
  • Deployment: Firebase hosting compatible

Project Structure

src/
├── ai/                 # AI flows and configurations
│   ├── flows/          # Individual AI capabilities
│   └── dev.ts          # Development server setup
├── app/                # Next.js app router structure
│   ├── (app)/          # Main application pages
│   │   ├── dashboard/   # Dashboard home page
│   │   ├── generate/    # Test case generation page
│   │   ├── manage/      # Test case management page
│   │   ├── import/      # Test case import page
│   │   └── compliance/  # Compliance checking page
│   ├── layout.tsx      # Root layout
│   └── page.tsx        # Home page redirect
├── components/         # Reusable UI components
│   ├── ui/             # Shadcn UI components
│   ├── TestCaseCard.tsx # Test case display component
│   └── providers.tsx   # Context providers
├── contexts/           # React context providers
├── lib/                # Utility functions and types
└── hooks/              # Custom React hooks

Core Functionality

1. Test Case Generation

  • Input: Product Requirement Documents (PRDs) in text format
  • Process: AI analysis of requirements to create structured test cases
  • Output: Test cases with Case ID, Title, Description, Steps, Expected Results, and Priority

2. Compliance Checking

  • Analyzes test cases against healthcare standards
  • Generates compliance reports
  • Identifies potential compliance gaps

3. Test Case Modification

  • Allows natural language modifications to existing test cases
  • AI-powered refinement of test scenarios

4. Import/Export Capabilities

  • Import existing test cases from various formats
  • Export functionality (currently demo-only for Jira, Azure DevOps, PDF)

AI Implementation

The system uses Google's Gemini AI through the LangChain framework:

  1. Model: gemini-2.5-flash via LangChain
  2. Flows:
    • generateTestCasesFromRequirementsFlow
    • complianceCheckTestCasesFlow
    • modifyTestCasesWithNaturalLanguageFlow

Performance Optimizations

  1. Next.js Optimizations:

    • Removed Turbopack for stability
    • Enabled SWC minification
    • Optimized image loading
    • Force dynamic rendering

  2. AI Response Optimization:

    • Simplified prompts for faster responses
    • Limited test case generation to 3 per request
    • Streamlined output formatting

Setup Instructions

  1. Environment Setup:

    npm install

  2. Environment Variables: Create a .env file with your Google AI API key:

    GOOGLE_API_KEY=your_google_ai_api_key_here

  3. Start Development Servers:

    # Start development server
npm run dev

# In a separate terminal, start Next.js frontend
npm run dev

  4. Access Application: Open http://localhost:9002 in your browser

Troubleshooting

Common Issues

  1. Port Conflicts:

    • Kill processes on port 9002 before starting servers
    • Use netstat -ano | findstr :9002 to find processes

  2. AI Generation Slow:

    • First requests may be slower due to model loading
    • Subsequent requests should be faster
    • Keep requirements documents concise

  3. Image Loading Issues:

    • Ensure images are in the public/ directory
    • Check image file extensions are supported

Performance Tips

  1. For Faster AI Responses:

    • Keep requirement documents under 2000 words
    • Use clear, specific language in requirements
    • Focus on key functionality in requirements

  2. For Better UI Performance:

    • Limit test cases to fewer than 50 per view
    • Use copy functionality instead of export for immediate needs
    • Refresh page if UI becomes unresponsive

Future Enhancements

  1. Integration Features:

    • Full Jira integration
    • Azure DevOps synchronization
    • Polarion ALM connectivity

  2. Advanced AI Capabilities:

    • Test data generation
    • Automated test script creation
    • Regression test identification

  3. Enhanced Compliance:

    • Detailed compliance matrices
    • Automated compliance gap analysis
    • Regulatory update notifications

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

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

Support

For support, please contact the development team or open an issue in the repository.