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# AI-Powered Test Automation System
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A comprehensive test automation system that leverages AI to generate, manage, and execute test cases from various document sources.
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## Features
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- Document Processing
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- PDF, Word, and scanned document support
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- OCR capabilities using Tesseract
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- Document segmentation for large files
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- Multi-stage processing pipeline
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- AI Integration
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- Support for multiple LLM providers (OpenAI, Local LLMs, OpenRouter)
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- Model benchmarking capabilities
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- Requirement extraction and analysis
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- Test case generation and validation
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- Test Management
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- Export to TestRail, JIRA, qTest
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- Test case validation metrics
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- Risk-based prioritization
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- Requirement traceability matrix
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- Coverage analysis
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- Automation
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- Test script generation (Python, Selenium, Playwright)
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- CI/CD pipeline integration
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- Docker containerization
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- RabbitMQ for async processing
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## Project Structure
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```
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testauto/
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βββ api/ # API endpoints
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β βββ routes/ # API route definitions
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β βββ middleware/ # API middleware
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βββ core/ # Core business logic
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β βββ document/ # Document processing
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β βββ ai/ # AI integration
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β βββ test/ # Test management
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β βββ automation/ # Test automation
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βββ services/ # Microservices
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β βββ document_service/
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β βββ ai_service/
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β βββ test_service/
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β βββ automation_service/
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βββ utils/ # Utility functions
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βββ tests/ # Unit and integration tests
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βββ config/ # Configuration files
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βββ docker/ # Docker configuration
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βββ frontend/ # Web interface
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```
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## Setup Instructions
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1. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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2. Set up environment variables:
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```bash
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cp .env.example .env
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# Edit .env with your configuration
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```
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3. Start the services:
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```bash
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docker-compose up -d
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```
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4. Run the application:
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```bash
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python run.py
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```
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## API Documentation
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The API documentation is available at `/docs` when running the application.
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## Required Documents for Features
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- Test Plan Generation: Project requirements document, scope document
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- Test Case Generation: Functional requirements, user stories
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- Risk Assessment: Project risk document, historical defect data
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- Test Data Generation: Data models, sample data
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- Defect Prediction: Historical defect reports, code metrics
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## Contributing
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Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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