Using a high-level test generation expert system for testing in-car networks

Abstract

The rising size and complexity of in-car networks call for more advanced and scalable verication solutions. We pro-pose a verication methodology for in-car networks based on a system level test generator tool used for creating mas-sive random biased stimuli, and on coverage and checking monitors. The test generator is an expert system based on an ontology of testing knowledge. A signicant challenge is the continuous nature of the stimuli needed to represent the physical environment and the state of the internal com-ponents controlled by the vehicle's electronic systems. We report on applying our methodology to an example in-car network simulator. Copyright 2014 ACM.