Abstract: An image of a graphical user interface is captured. A process is started to record identification of a graphical object (e.g., a text field) in the image of the graphical user interface. Recording identification of the graphical object in the image of the graphical user interface is based on machine learning. An end to the process to record identification of the graphical object in the image of the graphical user interface is identified. A machine learned software object is updated. For example, the machine learned software object is updated with a location of the graphical object. The updated machine learned software object then is used to test the graphical object. For example, to automatically fill in the text field using a test script.

Abstract: Embodiments of the disclosure provide systems and methods for performing functional testing on an application using artificial intelligence. According to one embodiment, a method for performing functional testing on an Application Under Test (AUT) can comprise maintaining, by a processor of an edge computing device, a model comprising a script defining one or more automatic tests for the AUT and trained data identifying objects of a user interface of the AUT. One or more tests can be executed on the AUT by the processor of the edge computing device based on the script and the trained data identifying the objects of the user interface of the AUT. Results of executing the one or more tests on the AUT can be provided by the processor of the edge computing device.

Abstract: Embodiments of the disclosure provide systems and methods for functional testing of an application based on evaluation of contents of a user interface of the application using artificial intelligence. Performing functional testing on an Application Under Test (AUT) can comprise building a model defining each of a plurality of object classifications for objects of a user interface of the AUT based on a graphical appearance of the objects. Objects in an image of the user interface can be identified based on the plurality of object classifications defined in the model and the graphical appearance of each of the one or more objects in the image. A test script defining one or more functional tests can then be executing on the AUT. Executing the test script can comprise performing the one or more functional tests on the AUT based on the identified one or more objects in the image.

Abstract: Embodiments of the disclosure provide systems and methods for performing functional testing on an application using artificial intelligence. According to one embodiment, a method for performing functional testing on an Application Under Test (AUT) can comprise maintaining, by a processor of an edge computing device, a model comprising a script defining one or more automatic tests for the AUT and trained data identifying objects of a user interface of the AUT. One or more tests can be executed on the AUT by the processor of the edge computing device based on the script and the trained data identifying the objects of the user interface of the AUT. Results of executing the one or more tests on the AUT can be provided by the processor of the edge computing device.