Abstract: A method for providing a test scenario simulation of an interaction of a plurality of vehicles by a computer system comprises receiving a scenario involving at least the plurality of vehicles, wherein the scenario is described in a high-level scenario description language; receiving a plurality of parameter values for the received scenario; modifying the plurality of parameter values according to at least one of a pass/fail predictor model and a pass/fail indication; narrowing a range of values of at least one parameter of the plurality of parameters by prediction of at least a key performance indicator (KPI) value using a KPI predictor; and generating a test scenario for the simulation of the interaction of the plurality of vehicles within the received scenario based on at least the narrowed range of values.

Abstract: A method for optimizing a verification regression includes obtaining data, by a processor, of previously executed runs of at least one verification regression session; extracting from the data, by the processor, values of one or a plurality of control knobs and values of one or a plurality verification metrics that were recorded during the execution for each of the previously executed runs of said at least one verification regression; finding, by the processor, correlation between said one or a plurality of the control knobs and each said one or a plurality of verification metrics, and generating a set of one or a plurality of control conditions based on the found correlation; and applying, by the processor, the generated set of one or a plurality of control conditions on the verification environment or on the DUT, or on both, to obtain a new verification regression session.

Abstract: A method including receiving a first configuration of a device validated against a design constraint, is provided. A configuration includes stimuli controls and stimuli parameters used as inputs in a device model. The method includes determining a quality of the first configuration based on an estimation of an output parameter including a desired behavior of the device, simulating the device in the first configuration when the first configuration quality overcomes a threshold, and requesting a second configuration of the device when the quality of the first configuration is below the selected threshold. The method also includes obtaining a regression based on multiple, high quality configurations to determine, for the device, a distribution of output parameter values and comparing the distribution of output parameter values with a baseline of a random regression to adjust the machine learning engine according to a target range of output parameter values.

Abstract: A method including selecting multiple input parameters of a device configuration environment to perform multiple simulations on an electronic device defined by the device configuration environment is provided. The method with multiple values for the multiple input parameters and a value of an output parameter resulting from the multiple simulations, and extracting a distribution of output parameter values and a distribution of input parameter values from a database. The method also includes finding a correlation involving the multiple input parameters and the output parameter based on a target range of the output parameter, and identifying an expected value of the output parameter using a range of values of the multiple input parameters in the correlation involving the multiple input parameters and the output parameter. A system and a nontransitory, computer-readable medium including instructions to perform the above method are also provided.