The widespread use of AI in testing has had profound effects on software testing and will continue to do so as it develops.
Generative AI has had a huge impact on the SDLC as a whole, but it’s not the only AI making an effect both now and in the future. AI affects every aspect of test automation from test authoring to reporting the results. While it’s important to not use AI as a crutch, it can provide proactive assistance to testers in many ways.
This eBook covers the effects of AI in the following areas:
Technology evolves pretty fast, and test automation right along with it. Frameworks were the industry standard for so long, and now with codeless test automation platforms coming into the market, it’s easy to lose track of how we got here and what we have. What’s more, with all of the software available, how do you know what makes a good test automation tool?
See anything you like there? We’ve got an excerpt for you to pique your interest, too:
“There was a time and a place for each type of testing software when technology like AI, ML, NLP, and RPA was not far advanced enough for codeless automation to even be possible. However, the need for quality at speed drives the development of codeless automation, as that need is one of the top objectives of QA teams. When new technologies and speed/scale requirements collide, that collision point creates codeless automation. Necessity breeds innovation.”