Abstract: In one example embodiment, a server compares derived values of a plurality of machine learning features with specified values of the plurality of machine learning features according to an ontology that defines a relationship between the plurality of machine learning features and a corresponding higher-order behavior for the specified values of the plurality of machine learning features. When the derived values of the plurality of machine learning features match the specified values of the plurality of machine learning features, the server aggregates the weights of the plurality of machine learning features to produce an aggregated weight. The server assigns the aggregated weight to the higher-order behavior so as to indicate a significance of the higher-order behavior in producing the machine learning result.

Abstract: In one example embodiment, a server compares derived values of a plurality of machine learning features with specified values of the plurality of machine learning features according to an ontology that defines a relationship between the plurality of machine learning features and a corresponding higher-order behavior for the specified values of the plurality of machine learning features. When the derived values of the plurality of machine learning features match the specified values of the plurality of machine learning features, the server aggregates the weights of the plurality of machine learning features to produce an aggregated weight. The server assigns the aggregated weight to the higher-order behavior so as to indicate a significance of the higher-order behavior in producing the machine learning result.

Abstract: A directed random verification system and method analyzes a pair of generated test cases, from a pool of generated test cases which are capable of testing at least a portion of an untested coverage event, and finds a logical, deterministic crossover point between at least two test cases. Once a pair of test cases with at least one crossover point has been identified the method crosses a portion of the random number trace up to the crossover point with a portion of the second random number trace, which continues from the crossover point. The result is a new random number trace that is a combination of a portion of one test and a portion of another test. The new random number trace is sent to the stimulus generator as the new random number input.

Abstract: A design verification method, comprising providing a circuit design; creating a stimulus tree diagram for the circuit design, wherein the stimulus tree diagram comprises L stimuli, M checkpointed splits, and N non-checkpointed splits; and executing the stimulus tree diagram, wherein said executing the stimulus tree diagram comprises, for i=1, . . . , M, executing an ith checkpointed split of the M checkpointed splits, wherein said executing the ith checkpointed split comprises (a) saving an ith context of an ith simulation environment in which said executing the stimulus tree diagram is performed; and (b) after said saving the ith context is performed, executing from the ith context along Pi paths of the stimulus tree diagram branching from the ith checkpointed split, wherein the ith checkpointed split is a Pi-way split, Pi being an integer greater than 1.