Quality assurance (QA) used to be a compliance activity. You were releasing a product and needed to test it and stamp it “approved.” QA was about testing that the code worked. You might manually test the code. You might have even tried some automation — coding a set of test scripts that would try to capture regressions or errors that you had eradicated in the past, but which somehow crept back in. All in all, you were reasonably satisfied that you achieved a level of test coverage that met your goals. Then, you put your code into production and crossed your fingers that nothing went wrong. And if it did, you tried to fix it as quickly as humanly possible.
It used to be that software testers could test their applications on just one platform, and only have to worry about testing that the code worked.
Everything about software has changed—how it’s architected, developed and produced, what it does, what users want from it, and how often they expect new features. To keep up, organisations are turning to continuous delivery and DevOps. Yet product teams still do a lot of manual testing, which consumes a lot of time they don’t have, thanks to shrinking test windows. Incorporating automation into your testing approach is a great strategy, but figuring out where and how to start isn’t necessarily quick and easy.
This blog is only partially about our newest iOS Gateway 5.0 release with device and simulator support for Touch ID and Face ID (which is super cool, but more about that later). It’s also a blog about how testing has changed — a lot — in a short amount of time.
We recently co-hosted a webinar with Bloor Research about the Future of Testing, and in it, we conducted an informal poll about artificial intelligence (AI) and testing. When we asked what everyone thought the biggest advantage was to incorporating AI into a test automation strategy, attendees overwhelmingly selected team productivity and efficiency.
The focus on artificial intelligence (AI) in general, in technology, and particularly in testing, is prompting organizations worldwide to take it seriously. It’s hard to ignore AI’s potential benefits, including improved productivity and efficiency, fewer defects, a better UX, and happy customers. And with DevOps and continuous delivery here to stay, staying relevant depends on keeping pace, which is why test automation is so critical.