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Optional software-based features (for example, to provide active safety, infotainment, etc.) are increasingly becoming a significant cost driver in automotive systems. In state-of-the-art production techniques, these optional features are built into the vehicle during assembly. This does not give the customer the flexibility to choose the specific set of features as per their requirement. They either have to buy a pre-bundled option that may or may not satisfy their preferences or are unable to find an exact combination of features from the inventory provided by a dealership. Alternatively, they have to pre-order a car from the manufacturer, which could result in a substantial delay. Therefore, it is important to improve the flexibility of delivering the optional features to customers. Towards this objective, the vehicle could be configured with the desired options at the dealership, when the customer requires them.

For many crucial applications, establishing important properties of Simulink models by testing is either extremely resource intensive or impossible, and proof of the properties is highly desirable. Many Simulink models rely upon discrete-valued functions for which the function values are defined as a lookup table of correspondences between values in the domain and range, with linear interpolation used to evaluate intermediate values in the domain. Such discrete-valued functions arise in applications for which no known closed-form algebraic definition exists. In general, the proof of a property for a model that includes a discrete-valued function has to be by case analysis. For a single function and with mechanical support, case analysis is manageable. However, for models that include multiple discrete-valued functions, the number of cases can be the product of the cardinalities of the domains of the individual functions.