How Machine Learning Is Changing Test Automation

Everybody wants to be part of the next big thing, whether that’s some shiny new test management strategy or an emerging technology like Artificial Intelligence (AI). In fact, so many people want to be part of the AI movement that there’s a real tendency to describe things as AI when they’re really just automation or advanced analytics. This puts decision-makers on the test bench in something of a tricky spot—you need to perform your due diligence to make sure you’re getting what you think you are out of a solution.

When it comes to Machine Learning (ML)—a specific sub-concept within the larger AI-sphere—there’s less confusion. Why? Because ML refers to a particular set of processes that revolve around structuring and analyzing unstructured data, in order to find insights and draw conclusions. Thus, while there are potentially infinite ways AI might transform test automation, there are a few defined, concrete applications for ML in the test lab. This includes Quality of Experience (QoE) testing, SLA monitoring, and fraud detection, all of which can significantly contribute to achieving ROI for your test operations.  

What is Machine Learning?