Since 2010, significant advances in computing and data availability have resulted in even more breakthroughs in machine learning algorithms and infrastructure. In other words, we now have the capability, information, capability, and know-how to implement machine learning in many more contexts. Today, we rarely need to start from scratch when it comes to machine learning, as existing libraries and tools greatly simplify the process. All we need to know is how to deploy a machine learning library such as TensorFlow.
Quality Engineering possesses all of the necessary characteristics for machine learning application. It has the potential for big data, analytical questions, and/or gamification. When combined with organizations' growing need to analyze and make decisions more quickly in order to release more quickly, the environment is ripe for disruption and innovation. It is not a matter of whether machine learning will impact how we develop, test, and release software; rather, it is a matter of when and how. This can only be achieved through the adoption of a learning culture.
The vast majority of discussion so far has centered on how machine learning will eliminate tester jobs or why machine learning cannot eliminate any tester job. The reality lies somewhere in between. As with automation, machine learning enables machines to perform routine, low-level tasks. Organizations must transition away from best effort quality assurance and toward continuous quality improvement. Without machines, we will be unable to compete, and it is up to executives and managers to educate their employees about how machine learning can help them become not only better testers, but ultimately quality owners.