Testing AI … Before Testing With AI

A lot of testers are talking about how to use artificial intelligence (AI) or machine learning (ML) to be the next biggest thing in the test tooling industry. Many are in what seem to be a lemming-like hurry to abdicate their responsibilities to algorithms that will do their thinking for them. Those same testers, however, often have absolutely no idea how to actually test such systems in the first place. So let’s dive into this a bit.

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The Constraints of (Testing) History

As a specialist tester, one has been doing this since the early 1990s, it’s interesting to follow the contours of a notoriously fractious discipline. A discipline that is often populated by articulate but frustratingly argumentative practitioners. I say “frustratingly” not because argumentation is bad (it isn’t) but because that argumentation often turns into becoming an instinctive contrarian and a ruthless, rather than pragmatic, skeptic.

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An Ode to Testability, Part 5

In the previous post we ended up creating tests with a context. And that context was allowing us to bridge the gap between correctness and value while also continuing to put focus on testability. We saw some warning signs along the way but, overall, made progress. Here we’ll continue that progress and also start to see how while testability is something to strive for, just doing so by itself guarantees us very little.

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A Plea for Testability

Part of achieving quality in software means treating testability as a primary quality attribute. Once you do that, you can then adapt your requirements and development styles from that point of view. Whether you call that “agile”, “lean”, “scrappy” or whatever else is largely beside the point. The focus is on testability. But let’s talk about what that means.

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