AI makes testing faster, but quality assurance more complex
AI accelerates testing. But how do you assess systems that do not always produce the same result?
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AI accelerates testing. But how do you assess systems that do not always produce the same result?
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Shift-left is a movement in the testing world that I wholeheartedly support. Both TMAP and ISTQB address the same principle: the later you find a bug, the more expensive it is. On many assignments I have had to actively ask to join discussions about requirements and software implementation earlier. Bugs uncovered through substantive discussion in those meetings are pure gain.
Why? Because you can often ‘fix’ them in that same meeting by updating the documentation. Compare that with a bug already in the code and deployed to the development, test and acceptance environments. Fixing that bug requires a bug report, a developer's fix and another round of deployments to development, test and acceptance. The later you find a bug, the more expensive it is. That is a reason not only to have team members in different roles review documentation early, but also to move test automation as far left as possible.
If you can write useful tests that exercise the system as a whole before it reaches a test environment, those tests should take priority over tests that can only run in a test environment. In my view, shift-left therefore applies to all testing activities.
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