Building test automation frameworks that validate what matters most β data integrity across every layer.
My approach: if a transaction passes the UI but breaks in the database, the test suite failed, not the app.
| Area | Approach |
|---|---|
| Data Integrity | Verifying that what the user sees matches what the database stores |
| Cross-Layer Testing | Tests that don't stop at the API response β Web β API β DB |
| AI in QA | Using LLMs for failure analysis, not as a replacement for assertions |
| Infrastructure | Docker, CI/CD, and backend servers as part of the test ecosystem |
End-to-end automation framework verifying data integrity across Web, API, Mobile, and Database layers.
- 53 automated tests across 4 layers β Playwright Β· Requests Β· Appium Β· SQL
- Cross-layer E2E validation β Web UI β API β Database consistency checks
- AI-powered failure analysis with automated bug classification via Groq/Llama
- CI/CD pipeline with MySQL service container, dual backend servers, and Allure Reports on GitHub Pages
- Data-Driven Testing with CSV/JSON inputs, Set Theory validation, and performance benchmarking