AutoTestify

Advanced automated testing and code analysis platform that provides comprehensive GitHub repository analysis and API testing capabilities.

Features

🔍 GitHub Repository Analysis

• Code Quality Assessment: Analyze code structure, complexity, and maintainability
• Security Vulnerability Detection: Identify potential security issues
• Performance Analysis: Evaluate code performance and optimization opportunities
• Documentation Review: Check README quality and documentation completeness
• CI/CD Pipeline Analysis: Assess build configurations and workflows
• Visual Reports: Interactive HTML reports with charts and insights
• UI Validation: Automated screenshot capture and repository UI verification

🚀 API Testing

• Automated API Testing: Test REST APIs with various HTTP methods
• Response Validation: Verify status codes, headers, and response data
• Performance Metrics: Measure response times and throughput
• Test Report Generation: Detailed HTML reports with test results
• Multiple Environment Support: Test across different environments

📊 Reporting & Analytics

• Interactive Dashboards: Visual representation of analysis results
• Export Capabilities: Download reports in HTML format
• Historical Tracking: View past analysis results
• Email Integration: Send reports via email (Mailjet integration)

🎨 Modern UI

• Dark/Light Theme: Toggle between themes
• Responsive Design: Works on desktop and mobile devices
• Real-time Updates: Live progress indicators
• Intuitive Navigation: Easy-to-use interface

Installation

Prerequisites

• Python 3.8+
• Chrome/Chromium browser (for UI validation)
• Git

Setup

1. Clone the repository

   git clone <repository-url>
   cd Autotestify

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment variables

   cp .env.example .env
   # Edit .env with your API keys

4. Run the application

   python app.py

5. Access the application Open http://localhost:5000 in your browser

Configuration

Required Environment Variables

# Gemini AI API (for code analysis)
GEMINI_API_KEY=your_gemini_api_key

# Email Service (Mailjet)
MAILJET_API_KEY=your_mailjet_api_key
MAILJET_API_SECRET=your_mailjet_secret
MAILJET_SENDER_EMAIL=your_sender_email
MAILJET_SENDER_NAME=AutoTestify

# Flask Configuration
SECRET_KEY=your_secret_key
FLASK_ENV=production

Usage

GitHub Repository Analysis

1. Navigate to GitHub Analysis page
2. Enter the repository URL (e.g., https://github.com/user/repo)
3. Click Analyze Repository
4. Wait for analysis completion
5. View the generated report

API Testing

1. Go to API Testing page
2. Enter API endpoint URL
3. Select HTTP method (GET, POST, PUT, DELETE)
4. Add headers and request body if needed
5. Click Test API
6. Review test results and performance metrics

Viewing Reports

1. Visit Reports page
2. Browse all generated reports
3. Click View to open reports in new tab
4. Click Download to save reports locally
5. Use Delete to remove unwanted reports

Project Structure

Autotestify/
├── app.py                 # Main Flask application
├── config.py             # Configuration settings
├── requirements.txt      # Python dependencies
├── .env                  # Environment variables
├── services/            # Core services
│   ├── api_tester.py    # API testing functionality
│   ├── gemini_service.py # AI-powered code analysis
│   ├── github_service.py # GitHub integration
│   ├── report_generator.py # Report generation
│   └── email_service.py  # Email notifications
├── templates/           # HTML templates
│   ├── base.html
│   ├── index.html
│   ├── github_analysis.html
│   ├── api_testing.html
│   └── reports.html
├── static/             # Static assets
│   ├── css/           # Stylesheets
│   ├── js/            # JavaScript files
│   └── charts/        # Generated charts
├── reports/           # Generated analysis reports
├── screenshots/       # Repository screenshots
└── dist_protected/    # PyArmor protected version

Security

This project includes PyArmor protection for source code obfuscation:

• Protected Files: Core Python modules are obfuscated
• Runtime Protection: Dynamic code decryption
• Deployment: Use dist_protected/ for production

Technologies Used

• Backend: Flask, Python 3.8+
• Frontend: Bootstrap 5, JavaScript, HTML5/CSS3
• AI/ML: Google Gemini API
• Web Automation: Selenium WebDriver
• Email: Mailjet API
• Security: PyArmor code protection
• Charts: Plotly.js with advanced statistical analysis
• Performance: Connection pooling, intelligent caching
• Validation: Comprehensive configuration validation
• Icons: Font Awesome

Contributing

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

License

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

Support

For support and questions:

• Create an issue on GitHub

Changelog

v1.1.0 (Latest)

• Performance Optimization: Enhanced API testing with connection pooling and caching
• Advanced Analytics: 7-tier performance classification with P50-P99 percentiles
• Configuration Validation: Comprehensive config validation with auto-fix capabilities
• Error Handling: Improved error handling with graceful degradation
• Code Quality: Fixed unicode encoding issues and deprecated API parameters
• Security: Enhanced PyArmor protection with updated obfuscation
• Stability: Connection pooling reduces response times by 30-50%
• Caching System: Intelligent file-based caching with 60-90% hit rates

v1.0.0

• Initial release
• GitHub repository analysis
• API testing capabilities
• Report generation
• Dark/Light theme support
• PyArmor code protection