Keynote: AI & GenAI in Quality Engineering: Crawl, Walk, Run
summary
Roadmap for adopting AI in QA with concrete wins and examples starting from generation to agentic orchestration including self-healing automation and coverage analysis; claims include moving regression from three-week to three-hour regression in some teams and achieving 30–50% effort reduction in test preparation with human in the loop and LLM tuning to ~90% accuracy; recommends focusing on data quality, ML pattern mining, failure prediction, and building utility, super and orchestration agents with a human‑in‑the‑loop moving to human‑on‑the‑loop.
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chapters (16)
- 0:00Welcome
- 2:32AI & GenAI in QE overview
- 4:17Modern QE & AI predictions
- 6:23Evolution of quality engineering practices
- 8:53AI ROI adoption
- 10:18ML algorithms for testing
- 12:25GenAI use cases in QE
- 15:18Agentic AI & three-tier architecture
- 18:07Talent impact & future skills
- 20:36Q&A
- 21:18First steps in GenAI for QE
- 22:42AI adoption principles
- 24:48Crawl phase fundamentals
- 26:35Testers & AI co-pilots
- 27:59QA skills in 5 years
- 30:47Closing
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