A system and method are disclosed that enable automated testing of a user interface. The testing system includes a machine learning (ML) algorithm to test a user interface. The ML approach includes collecting training samples, creating an ML model and using the model to test the user interface. Training samples are collected by providing the users with diff files with the differences highlighted. A user is provided with options to specify if the differences are acceptable or not. The user classification and other attributes are used to train the model. When a new diff is created it may be fed to the trained network which results in prediction of acceptance or rejection (for example as "ACCEPT DIFF" or "REJECT DIFF") as output of the network. The system eliminates false positives in an automated way and thus reduces time spent by human inspectors to test user interface changes.
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Abolhassani, Hassan, "Machine Learning Approach For User Interface Testing", Technical Disclosure Commons, (December 12, 2017)