This project is a learning-driven prototype to explore how AI can be used in test automation beyond traditional scripted approaches.
Instead of relying only on predefined test cases, the framework attempts to:
- Observe the application
- Decide what to do next using AI
- Perform exploratory-style testing
- Capture and report findings
This is not a production-ready framework, but a working experiment to understand practical implementation.
- Dynamic DOM understanding
- AI-driven action decisions
- Basic exploratory testing flow
- AI-assisted bug detection (not only assertions)
- Screenshot-based visual validation
- Automatic bug reporting
- Test case generation in Excel
- Basic exploration memory tracking
- Allure reporting integration
- Jenkins-ready execution
- Python
- Playwright
- Pytest
- Allure Reports
- Ollama (LLM integration)
- Pandas / OpenPyXL (Excel handling)
ai_tester_project/
│
├── ai/ # AI logic (decision making, bug detection)
├── browser/ # Playwright actions, DOM extraction, screenshots
├── reporting/ # Bug reports, test case generation
├── config/ # Environment/config files
├── tests/ # Test execution files
├── run_agents.py # Entry point
└── requirements.txt
git clone <your-repo-link>
cd ai_tester_project
python -m venv venv
venv\Scripts\activate # Windows
pip install -r requirements.txt
playwright install
Make sure Ollama is running locally.
Example:
ollama run llama3
pytest run_agents.py --headed -s --alluredir=allure-results
allure serve allure-results
This project is Jenkins-ready.
Basic steps:
- Configure job with project path
- Install dependencies in build step
- Run pytest command
- Publish Allure results
- Can AI guide test execution instead of fixed scripts?
- Can we detect bugs without explicit assertions?
- How far can exploratory testing be automated?
- AI decisions are not always consistent
- Requires tuning of prompts and inputs
- Not suitable for production use yet
- Visual validation is basic (screenshot-based)
- Risk-based intelligent exploration
- Better visual comparison (baseline vs diff)
- Improved bug classification
- More stable AI decision-making
This project was built as a learning exercise with the help of AI tools and references. The goal was to understand integration and workflow rather than build everything from scratch.
Feel free to explore, raise issues, or share suggestions.