DEEP MACHINE LEARNING IN SOFTWARE TEST AUTOMATION

摘要

The present disclosure involves systems, software, and computer implemented methods for deep machine learning in software test automation. One example method includes capturing images of a web page during successful executions of a test script that tests the web page. A convolutional neural network (CNN) is trained using the captured images. The CNN is configured to determine whether an image input matches previously captured images. A determination is made that a particular execution of the test script has failed. A first image of the web page is captured at a time of the test script execution failure. The first image is provided to the CNN. An output of the CNN is received that indicates whether the first image matches previously captured images. A source of the test script execution failure is determined based on the output received from the CNN.