AI-Driven Test Automation
Exploring how AI agents can support the testing lifecycle — from test design to implementation, refactoring and review — while keeping the output trustworthy.
- Problem
- AI can generate test code quickly, but generated tests are only valuable if they are correct, meaningful, maintainable and aligned with business requirements.
- My role
- Applying AI-assisted testing in a financial-domain project: using GitHub Copilot, Claude, ChatGPT and Playwright MCP to improve how tests are designed, written and maintained, and checking that the generated output is correct and useful.
- Technical approach
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- AI-assisted test planning and generation
- Multi-agent orchestration
- Test implementation and refactoring
- Automated review and quality evaluation
- Engineering challenges
- The challenge is not generating test code, but validating the quality and usefulness of the output — and keeping AI-generated tests reliable and maintainable.
- Results
- Not publicly available — the project is owned by my client.
- Technologies
- Source / write-up
- No public link yet.