Spyros Katopodis: How You Should Be Using Agile and AI For Testing And Quality Assurance
summary
Zebra builds a voice‑assistant device and an in‑house annotation frontend for ML and moves testing earlier so regressions run weekly instead of at the end of a sprint, which cuts defects and enables more frequent, smaller releases. Automation stack: Robot Framework, Selenium, Cypress/Playwright, Postman, JMeter/Locust; AI tools: GitHub Copilot plus a CaseCraft → CaseToCode pipeline that generates Robot Framework tests from requirements; a custom Engineering Insights dashboard that combines Jira and TestRail. Results: higher coverage, saved time and money, measurable lift in velocity.
AI-written summary · about
chapters (7)
- 0:00Agile and AI in QA
- 4:53Zebra Technologies background
- 10:58Types of testing at Zebra
- 14:51QA challenges and Agile
- 21:07Team challenges and Scrum
- 31:34AI tools for test automation
- 39:07Q&A
related talks
16mTransforming Test Automation with AI and Machine Learning
46mQA Trends and Concerns - How to Stand Out Through Test Automation and Scrum
17mStaying Ahead: The Future of QA Leadership
42mThe Power of Human and AI Collaboration in Software Testing
47mPanel Discussion: QE Strategic Shift: What's Changing with AI, Automation, and Speed?
46mQA to QE: Scaling Quality with Ownership, Code, and Culture
28mTesting at the Speed of Thought: QA in the Era of GenAI apps
42mIt's Inevitable, Mr. Anderson: Machines, Humans, and the Future of Testing