The future of intelligent testing: Merging human insight with deep machine learning

Traditional test automation still comes with challenges. Scripted tests, Keyword frameworks and test recorders lack the flexibility required to adapt to rapidly changing applications because they generally bind to specific attributes of an element. Machine learning models partially address this by aggregating multiple attributes and using probabilistic models to self-heal tests, but those models require large data sets and often make the wrong selection. As “smart” as these systems become, the missing variable is the actual intent of the test itself. To truly achieve intelligent testing we need to harness both the power of human insight and deep learning.