Abstract: In one example, a system includes an electronic processor configured to receive telemetry data originating from a plurality of client applications. The telemetry data includes data points associated with errors associated with one or more operations. The electronic processor also classifies the telemetry data based a plurality of classes of data, converts the plurality of classes of data into one or more metrics based on a plurality of dimensions, and aggregates the metrics for the classes of data by all the dimensions. The electronic processor accesses a predictive scoring model for a stored metric associated with a dimension of interest, determines a prediction error associated with the dimension of interest, detects an anomaly based on an item selected from the group consisting of the prediction error and a static threshold, and transmits an alert message, generates a bug report, and stores the bug report in a database.

Abstract: In one example, a system includes an electronic processor configured to receive telemetry data originating from a plurality of client applications. The telemetry data includes data points associated with errors associated with one or more operations. The electronic processor also classifies the telemetry data based a plurality of classes of data, converts the plurality of classes of data into one or more metrics based on a plurality of dimensions, and aggregates the metrics for the classes of data by all the dimensions. The electronic processor accesses a predictive scoring model for a stored metric associated with a dimension of interest, determines a prediction error associated with the dimension of interest, detects an anomaly based on an item selected from the group consisting of the prediction error and a static threshold, and transmits an alert message, generates a bug report, and stores the bug report in a database.