What are the process or idealogies that you used to leverage AI in the Quality world? Could you share some insights so we could takeaway some key insights
If AI starts finding bugs in our code, should we log it as a defect or call it “self-improvement”?
If quality is a key objectivity, where does trust sit (or where it should sit) on that spectrum?
How quality testing can be maintained in AI ?
What architectural pattern would you use to design a scalable, fault-tolerant, and highly available microservice system?
In a world where AI can predict performance bottlenecks do human engineers become optimization experts, or risk forecasters?
Is Different AI suggested for different testing models/Framework ?