What Testers Need from Machine Learning and Artificial Intelligence
summary
Defines ML as software that offers suggestions and AI as software that makes choices, argues testers need tools that capture production logging, UI interactions and state changes, build models mapped to data, and turn exploratory walks into roadmaps. Recommends survey mode to capture paths, unattended mode to run background checks, and treats test automation as a modelling problem that proposes surprising paths and actionable suggestions rather than just executing scripts.
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chapters (8)
- 0:00Introduction
- 3:15Engineering relevance
- 13:31Software vs traditional engineering
- 25:24Tester thinking and models
- 32:01Exploratory testing
- 40:35Future tester tools
- 57:48AI/ML in testing vision
- 1:02:01Q&A
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