TestHub is an AI-driven, full-stack test management platform designed to enhance testing efficiency and quality. It targets software development teams by integrating AI for demand analysis, test case generation, API testing, and UI automation, offering a unified solution for the entire testing lifecycle.

How It Works

TestHub employs a Django backend and Vue 3 frontend architecture. Its core innovation lies in AI integration: it can parse requirement documents (PDF/Word/TXT) to automatically generate test cases and provides an AI assistant via Dify. A key feature is AI-driven UI automation using the browser-use framework, which interprets web pages to automate test execution. This approach leverages large language models (LLMs) for intelligent test artifact creation and execution, aiming for significant efficiency gains.

Quick Start & Requirements

• Prerequisites: Python 3.8+ (3.12 recommended), Node.js 18+, MySQL 8.0+. Browser drivers (ChromeDriver/GeckoDriver) are recommended for UI automation.
• Installation:
  • Backend: Clone the repository, create a virtual environment (python -m venv venv), install dependencies (pip install -r requirements.txt), configure .env with database and secret key settings, run migrations (python manage.py migrate), create a superuser (python manage.py createsuperuser), initialize locator strategies (python manage.py init_locator_strategies), and start the scheduler (python manage.py run_all_scheduled_tasks).
  • Frontend: Navigate to the frontend directory, install dependencies (npm install), and start the development server (npm run dev).
• Access: Frontend: http://localhost:3000, Backend API: http://localhost:8000, API Documentation: http://localhost:8000/api/docs/.

Highlighted Details

• AI-powered demand analysis and test case generation from various document formats (PDF/Word/TXT).
• AI-driven UI automation leveraging the browser-use framework, supporting text (DOM parsing) and vision (screenshot recognition, not yet implemented) modes.
• Enterprise-grade JWT security with dual tokens, automatic refresh, and token blacklisting.
• Support for multiple AI models including DeepSeek, Tongyi Qianwen, Google Gemini, and OpenAI, configurable per role.
• Comprehensive test management features including API testing (HTTP/WebSocket), UI automation (Selenium/Playwright), test case lifecycle management, and review workflows.

Maintenance & Community

The project welcomes contributions via Issues and Pull Requests. It is developed by "大刚 (公众号：测试开发实战)". No specific community channels like Discord or Slack are mentioned.

Licensing & Compatibility

The project is licensed under the MIT License, which is generally permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

The AI intelligent mode for UI automation notes that the vision mode (screenshot recognition) is not yet implemented. Python 3.12 is recommended, suggesting potential compatibility issues with other Python versions.