Systems | Development | Analytics | API | Testing

In this fireside chat with Jaydeep, we’ll dive into how AI is changing the way we measure success in both QA processes and live generative AI bots. On the QA side, we’ll look at cycle time reduction—the “time goalie” metric that shows how quickly we move from discovering a bug to fixing it. We’ll also talk about predictive quality accuracy, which shifts QA from being reactive to proactive by predicting which code changes are most likely to introduce bugs. And of course, we’ll touch on test creation velocity—how much faster teams are able to create meaningful automation with AI’s support.

Many customers were struggling with BDD-style test processes — writing structured, English-language scripts that business and tech teams could agree on but couldn’t easily automate. Before Katalon, they lacked a smooth way to turn those scripts or raw requirements into automated tests, which Katalon (and AI features like Studio Assist) finally made efficient. — Coty Rosenblath, Chief Technology Officer at Katalon.