Spent this week writing tests that fail on purpose. Only sometimes, of course. That’s the whole point 🙃
I am reworking my TestNet workshop around more realistic flaky scenarios: race conditions, optimistic UI, background jobs, shared test data.
AI features for testing:
1. Self Healing Tests
2. Test Code Generation (using LLM or migrate test code to another language, GitHub Copilot)
- Create test code from comments
- Create complete test scripts from test input
4. Code Explanation
- Explain code
- Generate code documentation
5. Visual Checks
6. Natural Language Processing
- NLP tools use AI to transform text into commands
- Can be used to describe single steps
- AI can generate a complete automated script from user story
3. Test Case Genertaion
- Generate Test Data
- Generate test cases using LLM
- Based on this information they can generate test cases from user stories, descriptions, or acceptance criteria
Main advice for testers by Filip Hric: go and write your own app, it could give you a better context and make you better in making decisions while building an automation testing strategy
https://t.co/cc22TLqyXs
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