30 Days of AI in Testing Challenge: Day 7: Research and share prompt engineering techniques
Share prompt engineering resources and practical advice for stating context, constraints, goals, and expected outputs clearly.
#30 Days of AI in Testing
30 Days of AI in Testing Challenge: Day 6:Explore and share insights on AI testing tools
Summarize Katalon Studio's advertised AI test authoring, code explanation, and self-healing features, along with the author's reasons for considering the tool.
30 Days of AI in Testing Challenge: Day 5:Identify a case study on AI in testing and share your findings
Summarize a Meituan–Fudan case study that combines UI text, visual content, and component-tree attributes to detect interaction intent.
30 Days of AI in Testing Challenge: Day 4: Watch the AMA on Artificial Intelligence in Testing and share your key takeaway
Summarize an AI testing AMA on bias, data security, everyday QA uses, AI performance monitoring, and how tester responsibilities may change.
30 Days of AI in Testing Challenge: Day 3: List ways in which AI is used in testing
List five AI testing uses from the author's research: test data generation, defect prediction, visual testing, QA knowledge bases, and test-tool development.
30 Days of AI in Testing Challenge: Day 2: Read an introductory article on AI in testing and share it
Review AppAgent, a multimodal smartphone agent that learns apps through exploration or demonstrations, and consider its potential and limits for mobile UI testing.
30 Days of AI in Testing Challenge: Day 1: Introduce yourself and your interest in AI
Share the author's QA background, interest in AI testing, and goals for learning how AI can improve manual and automated testing.