Split Software, Inc.

Patricio Echagüe

Trevor Stuart

Henry Jewkes

Andrew Greene

Elizabeth Eardley

Adil Aijaz

Pierre-Alexandre Masse

Sophie Harpur

A method and apparatus for event attribution during software experimentation is described. The method may include receiving, by a server computer system, a plurality of event tracking messages associated with an end user system, each event tracking message including at least a customer identifier, an end user identifier, and a timestamp. The method may also include storing each event tracking messages with the received customer identifier, end user identifier, and timestamp in a customer data store. Furthermore, the method may include applying, by the server computer system, a feature treatment to a configurable application executing on the end user system, the feature treatment specified by a customer system associated with the customer identifier, and the feature treatment configures one or more features of the configurable application associated with the end user identifier. The method may also include attributing a first set of events from the received plurality of event tracking messages with the application of the feature treatment.

Systems and methods for providing event attribution in software application

A method and apparatus for enabling control of execution of software applications is described. The method may include receiving an identifier for a user system, an identifier of a feature of an application running on the user system, and one or more definition values that define a treatment to be applied to the application feature. The method may also include storing the identifier for the user system, the identifier of the feature of an application running on the user system, and the definition values to be applied to the application feature. Furthermore, the method may include applying the definition values to the application feature at the user system based on the identifier for the user system stored in the memory, wherein the definition values configures the feature within the application while the application is running at the user system.

Systems and methods for providing control of application execution

A method and apparatus for configurable application feature experiments is described. The method may include receiving a set of metrics to be collected after a feature treatment is deployed to configurable applications executed by a plurality of end user systems, a significance threshold for detection of feature treatment impact on one or more metrics within the set of metrics, and a request to perform multiple comparison correction when detecting the feature treatment impact. The method may also include receiving, from the configurable applications, event messages that include metric values associated with the set of metrics. Further, the method may also include performing a statistical analysis of the metric values to determine whether the feature treatment caused a statistically significant change in values associated with one or more of the metrics, where the statistical analysis adjusts one or more parameters used to perform the statistical analysis based on a multiple comparison correction analysis.

Systems and methods for multiple comparison correction during configurable application feature experiments

Systems and methods for providing event attribution in software applications

A method and apparatus for application anomaly detection and remediation is described. The method may include receiving a plurality of event tracking messages generated by configurable applications after a feature treatment is deployed to configurable applications running on a first set of end user systems, and associating the feature treatment with values of a metric from the event messages that are attributable to the feature treatment being executed by the one or more configurable applications. The method may also include determining an impact of the feature treatment on the degradation of the metric when compared to a control value of the metric determined from a second plurality of end user systems that are not exposed to the feature treatment. Then method may further include, in response to detecting the statistically significant degradation of the metric, performing one or more actions to remediate the undesired impact of the feature treatment on the execution of the application.

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Redwood City, CA, United States of America

Patricio Echagüe