test-automator

iamsnh01/test-automator

3.2

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Test Automator is a comprehensive testing automation platform powered by Large Language Models, designed to streamline the process of generating, executing, and analyzing software tests.

Tools
  1. generate_tests

    Generates intelligent tests for your codebase.

  2. run_tests

    Executes generated tests.

  3. analyze_test_report

    Provides LLM-powered insights from test results.

šŸ¤– Test Automator

A comprehensive, intelligent, and extensible testing automation platform powered by Large Language Models. Test Automator streamlines the process of generating, executing, and analyzing various types of software tests (unit, integration, end-to-end, API) for both web UI and backend logic.

Python 3.11+ License: MIT MCP Compatible Windows Compatible

✨ Features

🧠 LLM-Powered Test Generation

  • Intelligent Analysis: Automatically analyzes your codebase to understand structure and dependencies
  • Smart Test Cases: Generates comprehensive test scenarios including edge cases and error conditions
  • Context-Aware: Understands your code patterns and generates idiomatic tests
  • Multi-Language Support: Optimized for Python with extensible architecture

šŸ”§ Comprehensive Test Types

šŸ”¬ Unit Testing
  • Analyzes individual functions, methods, and classes
  • Generates pytest test functions with proper fixtures
  • Includes positive, negative, and edge case scenarios
  • Handles both sync and async code patterns
šŸ”— Integration Testing
  • Tests interactions between different modules and services
  • Simulates real component interactions
  • Uses appropriate mocking strategies
  • Tests configuration and initialization flows
🌐 End-to-End (E2E) Testing
  • Browser automation using Playwright and browser-use
  • Simulates real user interactions
  • Tests complete user workflows
  • Captures screenshots and generates visual reports
šŸš€ API Testing
  • Comprehensive HTTP endpoint testing
  • Request/response validation
  • Authentication and authorization testing
  • Performance and timeout testing

šŸ“Š Intelligent Reporting

  • LLM-Enhanced Analysis: AI-powered insights from test results
  • Multi-Format Support: XML, JSON, and HTML report parsing
  • Actionable Recommendations: Specific suggestions for improvement
  • Risk Assessment: Identifies critical areas needing attention

šŸ› ļø Installation

Prerequisites

  • Python 3.11+
  • Google API Key (for Gemini LLM)
  • Claude Code or Cursor with MCP support
  • Git

Quick Install

Windows (Native or WSL)
# Clone the repository
git clone https://github.com/your-repo/test-automator.git
cd test-automator

# Create virtual environment
python -m venv .venv
.venv\\Scripts\\activate  # Windows
# or
source .venv/bin/activate  # WSL/Linux

# Install dependencies
pip install -e .

# Install Playwright browsers
playwright install
Linux/macOS
# Clone and setup
git clone https://github.com/your-repo/test-automator.git
cd test-automator

# Create virtual environment
python3.11 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -e .

# Install Playwright browsers
playwright install

āš™ļø Configuration

1. Get Google API Key

  1. Visit Google AI Studio
  2. Create a new API key
  3. Set environment variable:
    # Windows
    set GOOGLE_API_KEY=your_api_key_here
    
    # Linux/macOS/WSL
    export GOOGLE_API_KEY=your_api_key_here
    

2. Configure Claude Code

Option A: CLI Configuration
claude mcp add test-automator "/path/to/test-automator/.venv/bin/test-automator" -e "GOOGLE_API_KEY=your_api_key"
Option B: Manual Configuration

Add to your Claude Code configuration (~/.claude.json):

{
  "projects": {
    "/your/project/path": {
      "mcpServers": {
        "test-automator": {
          "type": "stdio",
          "command": "/path/to/test-automator/.venv/bin/test-automator",
          "env": {
            "GOOGLE_API_KEY": "your_api_key_here"
          }
        }
      }
    }
  }
}
Windows Path Examples
  • Windows: C:/path/to/test-automator/.venv/Scripts/test-automator.exe
  • WSL: /home/username/test-automator/.venv/bin/test-automator

šŸš€ Usage

MCP Tools Available

generate_tests(code_path, test_type="all")

Generate intelligent tests for your codebase:

# Generate all test types
generate_tests("/path/to/your/code", "all")

# Generate specific test type
generate_tests("/path/to/your/api.py", "unit")
generate_tests("/path/to/your/project", "integration")
generate_tests("/path/to/your/webapp", "e2e")
generate_tests("/path/to/your/api", "api")
run_tests(test_type="all", target_path="tests/")

Execute generated tests:

# Run all tests
run_tests("all", "/path/to/tests")

# Run specific test type
run_tests("unit", "/path/to/tests")
analyze_test_report(report_path)

Get LLM-powered insights from test results:

analyze_test_report("/path/to/test_results.xml")

Example Workflow

# 1. Generate comprehensive tests
generate_tests("/home/user/my-project", "all")

# 2. Run the tests
run_tests("all", "/home/user/my-project/tests")

# 3. Analyze results
analyze_test_report("/home/user/my-project/tests/results/unit_results.xml")

šŸŽÆ Advanced Features

Large Codebase Support (100k-200k lines)

  • Modular Analysis: Processes code in manageable chunks
  • Incremental Testing: Generates tests incrementally for better performance
  • Parallel Execution: Supports pytest-xdist for parallel test runs
  • Smart Filtering: Focuses on testable units to avoid overwhelming LLM

Cross-Platform Compatibility

  • Windows Native: Full support with proper path handling
  • WSL Integration: Seamless Windows Subsystem for Linux support
  • Linux/macOS: Native Unix support
  • Event Loop Handling: Platform-specific async optimizations

Performance Optimizations

  • Async Operations: Non-blocking test execution
  • Batch Processing: Efficient handling of multiple test files
  • Resource Management: Proper cleanup and memory management
  • Timeout Handling: Configurable timeouts for different test types

šŸ“ Project Structure

test-automator/
ā”œā”€ā”€ test_automator/
│   ā”œā”€ā”€ __init__.py
│   ā”œā”€ā”€ mcp_server.py          # Main MCP server with tools
│   ā”œā”€ā”€ test_generator.py      # LLM-powered test generation
│   ā”œā”€ā”€ test_runner.py         # Cross-platform test execution
│   └── report_analyzer.py     # AI-enhanced report analysis
ā”œā”€ā”€ pyproject.toml             # Package configuration
└── README.md                  # This file

šŸ”§ Troubleshooting

Common Issues

"GOOGLE_API_KEY not found"
# Set the environment variable
export GOOGLE_API_KEY="your_api_key_here"

# Or add to shell profile
echo 'export GOOGLE_API_KEY="your_api_key"' >> ~/.bashrc
"Playwright browsers not found"
# Install browsers
playwright install

# Install system dependencies (Linux)
playwright install-deps
Windows Permission Issues
# Ensure script execution is enabled
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

# Check virtual environment activation
.venv\\Scripts\\activate
WSL Display Issues (for E2E tests)
# Install X11 server for Windows
# Add to ~/.bashrc:
export DISPLAY=:0.0

šŸ¤ Contributing

  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

šŸ“„ License

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

šŸ™ Acknowledgments

  • Gemini LLM: Google's powerful language model for intelligent test generation
  • Playwright: Cross-browser automation framework
  • pytest: Robust Python testing framework
  • MCP Protocol: Model Context Protocol for seamless AI integration
  • Claude Code: AI-powered development environment