Testing and AI

What’s been interesting in the testing world — at least the part of it that I hang out in — is the application of different AI-based learning algorithms to the act of exploring an application and seeing what (if anything) that tells us regarding the algorithmic and non-algorithmic parts of the testing discipline. Let’s talk about this because I think is fertile ground for testers to be exploring.

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Solution Development in Python, Part 1

It’s been awhile since I tackled anything too traditionally “technical.” Lately I’ve encountered many testers who are interested in using Python as their ecosystem of choice for test solutions, particularly in data science or machine learning environments. So here I’ll talk about being a test solution developer in a Python context and what it means to create solutions in this ecosystem.

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Testing and Model Building

In his book The Black Swan, Nassim Taleb talks about “Platonicity,” which is defined as the desire to cut reality into crisp shapes. This is a form of dividing up a large domain into a smaller domain. This, by definition, means establishing certain boundaries. This is a key part of how people experiment and thus of how they model … and thus of how they ultimately explain things. So let’s talk about what this has to do with testing.

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