Abstract: Examples of the present disclosure describe systems and methods for selecting test combinations of hardware and software features for product validation. In examples, a test scheduler of a system receives metrics associated with a first batch of test combinations for multiple computing entities. The metrics may include data associated with a fleet prevalence and a fleet risk determined for the multiple computing entities. The scheduler provides one or more of the metrics to a machine learning algorithm. The test scheduler determines a second batch of test combinations associated with a subset of the multiple computing entities based on an output of the machine learning algorithm. The second batch of combinations may include a subset of the first batch of test combinations. The system executes the second batch of test combinations for the subset of the multiple computing entities.

Abstract: Examples of the present disclosure describe systems and methods for selecting test combinations of hardware and software features for product validation. In examples, a test scheduler of a system receives metrics associated with a first batch of test combinations for multiple computing entities. The metrics may include data associated with a fleet prevalence and a fleet risk determined for the multiple computing entities. The scheduler provides one or more of the metrics to a machine learning algorithm. The test scheduler determines a second batch of test combinations associated with a subset of the multiple computing entities based on an output of the machine learning algorithm. The second batch of combinations may include a subset of the first batch of test combinations. The system executes the second batch of test combinations for the subset of the multiple computing entities.