Abstract: A method of software launch regression testing comprises monitoring a user engagement parameter of an existing application running on a plurality of client devices and determining a probability interval from the user engagement parameter of the existing application. A candidate update application is then launched to a subset of the plurality of client devices. The method then proceeds with monitoring a corresponding user engagement parameter of the candidate update version running on the subset of client devices, determining if the corresponding user engagement parameter of the candidate update version falls within the probability interval, and, based on the probability interval falling within the probability interval, providing a testing pass notification.

Abstract: A method of software launch regression testing comprises monitoring an operational parameter of an existing application running on a plurality of client devices and determining a probability interval from the operational parameter of the existing application. A candidate update application is then launched to a subset of the plurality of client devices. The method then proceeds with monitoring a corresponding operational parameter of the candidate update version running on the subset of client devices, determining if the corresponding operational parameter of the candidate update version falls within the probability interval, and, based on the probability interval falling within the probability interval, providing a testing pass notification.

Abstract: A method of software launch regression testing comprises monitoring an operational parameter of an existing application running on a plurality of client devices and determining a probability interval from the operational parameter of the existing application. A candidate update application is then launched to a subset of the plurality of client devices. The method then proceeds with monitoring a corresponding operational parameter of the candidate update version running on the subset of client devices, determining if the corresponding operational parameter of the candidate update version falls within the probability interval, and, based on the probability interval falling within the probability interval, providing a testing pass notification.

Abstract: Described herein is a method of predicting production rates of one or more wells configured to extract petroleum from a petroleum reservoir, the production rates affected by one or more injectors configured to inject water into the petroleum reservoir, the method comprising: calculating a relationship parameter, using a plurality of models, for each of the one or more wells and an associated one of the one or more injectors; predicting future values of the relationship parameter calculated using the plurality of model; calculating a weighted aggregate of the future values of the relationship parameter, wherein weights for the future values are those that minimize a prediction error; predicting the production rates using the a weighted aggregate.