Abstract: Network alert detection utilizing trained edge classification models is described. An example of a computing system includes a processor and a memory storing instructions that cause the processor to train one or more classification models at a core for detection of signatures based on training data derived from a set of error codes; deploy the one or more trained classification models at an edge of a network; receive alerts from one or more nodes in one or more clusters of nodes in the network; detect one or more signatures by processing the received alerts at the one or more trained classification models; and perform one or more actions to address a signature that is detected by the one or more trained classification models.

Abstract: Examples include generating log line signatures for log lines of a plurality of log files, converting log line signatures to dimensional vectors, generating log file vectors and identifying one or more subsystems associated with each log file, based on a log file vector associated with the corresponding log file and a classification model.

Abstract: The present disclosure includes systems and methods for a selective regression testing. One method for selective regression testing includes grouping a number of test cases into a number of groups, analyzing a modification that is to be accomplished on a computing system to determine a level of risk of the modification to the computing system, applying one or more rules to determine which groups of test cases to apply to test the modification based upon the determined level of risk, and selecting one or more of the groups of test cases based upon the application of the one or more rules.