Exploratory Testing with AI
summary
AI generates focused session-based testing charters and prioritized test lists in seconds, uses Claude to suggest API and UI charters, and auto-creates JSON inputs and Playwright Python code that often needs adjustments. It can generate Playwright tests from manual exploration and refactor to Page Object Model to fit existing frameworks, and it drafts reproduction scripts and expected-response fixtures for Star Trek API examples. Human judgment validates flaky outputs, filters dynamic fields, and decides what to lock into regression.
AI-written summary · about
chapters (12)
- 0:00Exploratory testing with AI
- 3:00What is exploratory testing
- 7:37AI vs manual testing roles
- 10:51AI test charters for UI and APIs
- 18:24Prioritizing test charters with AI
- 21:33Next steps with AI exploration
- 25:47Automated tests from exploration
- 29:22Refactoring tests for automation
- 31:13AI input data for API testing
- 34:19Expected API response results
- 38:14Key takeaways and AI future
- 41:11Q&A
related talks
39mHow to Use AI with Playwright for UI & API Testing
55mAI-First Testing
35mApp Testing with AI Tools – Make it Easy
26mAI and the Future of Testing
01h 05mPanel Discussion - The Future of Testing: Impact of AI in Quality Assurance and Beyond
48mSupercharged Testing: AI-Powered Workflows with Playwright + MCP
32mQuality engineering in the AI age: From testing AI to leveraging GenAI
33mShray Sharma: Leveraging AI for Self-Healing Tests: Current Capabilities and Future Prospects