Facebook, Inc.

Eun Chang Lee

Michael M Magruder

Zheng Mi

Jeffrey Scott Dunn

Joel F Beales

Kumar Rangarajan

Hangjin Zhang

David Ross Harrington

Lifei Huang

Xun Li

A system and method that automatically detects that a regression incident has occurred, and identifies the particular revision(s) of source code that introduced the regression incident is disclosed herein. The system and method execute one or more simulations of each revision in a window of revisions of the source code to compute values for one or more metrics. The metric values are then used to compute regression confidence values as well as a regression confidence interval&#x2014;to then detect a regression incident and identify the revision that caused the regression incident. The system and method can also identify subsequent revision(s) that sufficiently fix regression incident(s) caused by a revision such that a software developer can be informed of these fixing revisions.

Incident tracker

A cluster of devices can be identified where results from executing a test by any cluster devices can be considered as being from the same device. Thus, instead of waiting for a single device to produce comparable results, multiple devices from the same cluster can simultaneously perform the test and obtain the needed set of test results more quickly. The technology can identify clusters of devices that are all similar to a primary cluster device. A device pair can be considered similar when (1) a mean each of a set of test results from each device are within a first threshold of each other, (2) a measurement of the consistency of each test result set are within a second threshold of each other, and (3) a measurement of the consistency of a combination of the test results sets is between the consistency measurements of the individual test result sets.

Dynamic device clustering

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San Mateo, CA, United States of America

Eun Chang Lee