All right! How is that for a title ;)? I give props for coming out swinging and yes, I am indeed curious as to whether or not AI will actually have a long-term impact on what I do as a tester or automation programmer. It feels weird to say that but since "Senior Automation Engineer" is my official title, yeah, I kind of care about this topic :).
Many tools are built around allowing us to automate certain steps but in general, automation excels in the areas of the rote and the everyday repeatable. Automation is less good at dynamic environments and where there's a lot of variabilities. However, perhaps a better way to think about it is that automation struggles with areas that *we* feel are dynamic and not rote. Machine Learning can actually help us look for the repeatable, perhaps more specifically in areas that we are not currently seeing those patterns.
We are seeing the growth of pattern recognition tools and visual validation. As we get further into the process, we see that there are more uses for visual validation tools. It's not just is the picture in the same place. My question would be how can we leverage these tools for more approaches? As in most situations, a lack of imagination is not necessarily a lack of adventure or spirit but more often a lack of relevant experience. We tend to get mired in the specific details and we tend to look at these little tedious issues as taking too much time, requiring too many steps, or not being flexible enough to actually be useful.
Lisette makes the case that AI can help with API tests. Since API tests can be bounded (as in there's a set of commands and those commands can take a set of parameters, etc. So they can be generated and they can be called. In addition, auto-healing tests are often touted as a step forward., I will confess I have seen self-healing tests in only a limited capacity but that has more to do with what we use and what we currently test with rather than what is available and usable. I want to see more of this going forward and interact with it in a meaningful way.
I hear often the idea that AI will help to create more reliable automated tests. This comes down to agent counts keeping track of what is right or wrong by the definition of the software. It sounds cool on the surface but again, I'd love to see it in action. Lisette makes a good point that automation for the sake of automation doesn't really buy us anything. Our automation needs to serve a purpose so putting these AI tools to help us find critical paths or critical workflow steps and processes is worth the time. Again, I'm willing to give it a go :).