We’ve all heard about AI for software testing from some seriously smart people, but there has been a lot of confusion about the idea. This article tackles some of the questions you might be asking: Do I need to be a genius to use AI for software testing? Is AI going to replace me as a tester? Where does AI fit into my testing strategy? With a simple analogy of training a dog, learn how AI fits into testing.

The higher you climb in the organization, the less information you get: An executive might only see red, yellow, and green for a project. Any time different teams need to communicate complex information, there is bound to be some information loss, and maybe some information control. We just need to understand where and why that happens, and—hopefully—how we can mitigate it.

As your QA team grows, manual testing can lose the ability to focus on likely problem areas and instead turn into an inefficient checkbox process. Using machine learning can bring back the insights of a small team of experienced testers. By defining certain scenarios, machine learning can determine the probability that a change has a serious defect, so you can evaluate risk and know where to focus your efforts.

In the era of agile and DevOps, release decisions need to be made rapidly—preferably, even automatically and instantaneously. Test results that focus solely on the number of test cases leave you with a huge blind spot. If you want fast, accurate assessments of the risks associated with promoting the latest release candidate to production, you need a new currency in testing: Risk coverage needs to replace test coverage.