Microsoft Technology Licensing, LLC

Dipti A Patil

Jin S Yu

Tatiana Shubin

Matthew Spencer Donald Kerr

Ganesh Sankarakumar Parvathy

Shaker Asif Khaleque

Kelly John Forsmann

Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.

Reinforcement learning applied to survey parameter optimization

Automated extensible user interface testing supports testing of a user interface of a program. Test data is accessed, the test data including multiple test steps. Each test step describes at least a part of a test to be performed on the user interface. For each of the multiple test steps, one or more application program interface (API) methods to invoke to carry out the part of the test is determined. This determination is based at least in part on the test data and on an identification from the API of methods supported by the API. Each of the one or more API methods is then invoked to carry out the part of the test. Verification can be performed to ensure, for example, that specified files were created, or registry values were changed, or user interface elements appear and exist.

Automated extensible user interface testing

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Sammamish, WA, United States of America

Dipti A Patil