Today, AI based on neural networks is at a very interesting stage of its development. It has clearly taken off: we see numerous applications from reading CT scans to picking fruits. But adoption rates vary a lot. Recommendation engines, customer support bots, and other stuff that's been called ”internet AI” are fairly widespread; we see it everywhere in our lives. However, most areas, including software testing, aren’t quite there yet.
What makes AI widely adopted and what’s tripping it up? Let’s take a look at our own field, software testing. Specifically, I want to see how well neural networks can generate automated tests, and what barriers there are to mass adoption of AI in our field. I’ll be looking first and foremost at the problems and difficulties not because I’m trying to be glass-half-empty, but because things that go wrong always tell us more about how a system works than things that go right.