I’d set out to understand how useful AI could be for creating UAT tests. Instead, my study exposed a question I hadn’t thought to ask: was the project suitable for AI in the first place? Why I thought I had found the ideal project My study methodology was designed around a few basic principles: compare like withContinue reading “The AI Study That Changed the Question”
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Moving from “why measure AI tests?” to “How do we measure them?”
In my previous blog post, I laid out why I wanted to evaluate how useful AI could be in my test creation process. I wanted evidence I could use to make informed and defensible decisions about what worked well and conversely what didn’t. As testers we should not run vibes, we should work on evidence.Continue reading “Moving from “why measure AI tests?” to “How do we measure them?””
Why We Measured AI Instead of Trusting It
Generating test cases with AI was easy. Working out whether they added value was much harder. This blog series grew out of a conference presentation exploring how AI-generated UAT test cases could be evaluated through evidence rather than assumptions. Where we started AI is everywhere. We’re told AI will revolutionise everything bringing unparalleled efficiency andContinue reading “Why We Measured AI Instead of Trusting It”
