The QA spectrum
summary
Argues QA is a measurable spectrum with five maturity states including AI ready operations and shows a banking case where an assessment finds process and tools strong but AI and innovation and people culture weak. Recommends a binary checklist assessment, three-sprint iteration to trial tools (keep if it improves metrics, two more sprints to confirm, otherwise drop), and three practices: three-amigo sessions, team self-reflection, and treating automation as a product. Demonstrates emerging tools: visual testing/vibe that graphs clicks, IDE test libraries like Junu, and LangChain workflow validation; trust metrics (KPMG ~50% trust) drive multidisciplinary governance.
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chapters (8)
- 0:00Introduction
- 2:30QA is a spectrum not binary
- 5:48Five QA maturity states
- 8:37Three practices for QA maturity
- 12:11AI testing versus testing AI
- 16:29QA testing AI systems today
- 19:44Why QA must embrace AI
- 22:41Call to action for QA
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