Microsoft Technology Licensing, LLC

Connie Qin Yang

Xiaoyu Chai

Steve K Lim

Logan Ringer

Nithin Adapa

Matthew Scott Rosoff

Igor B Peev

Stephen C Rowe

Steven Marcel Elza Wilssens

Alexander S Weil

Tomas Aleksas Mereckis

Eamon Guthrie Cosgrove Millman

Soheil Sadeghi

Omari Carter-thorpe

Disclosed herein is a system for leveraging telemetry data representing usage of a component installed on a group of sampled computing devices to confidently infer the quality of a user experience and/or the behavior of the component (e.g., an operating system) on a larger group of unsampled computing devices. The system is configured to use a propensity score matching approach to identify a sampled computing device that best represents an unsampled computing device using configuration data that is collected from both the sampled and unsampled computing devices. The quality of the user experience and/or the behavior of the component may be captured by a metric of interest (e.g., a QoS value). Accordingly, the system is configured to use the known metric of interest, determined from the telemetry data collected for the sampled computing device, to determine or predict the metric of interest for the unsampled computing device.

Using propensity score matching to determine metric of interest for unsampled computing devices

Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.

Feature deployment readiness prediction

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Seattle, WA, United States of America

Connie Qin Yang