Using a high-level test generation expert system for testing in-car networks
Allon Adir, Alex Goryachev, Lev Greenberg, Tamer Salman · 2014
The rising size and complexity of in-car networks call for more advanced and scalable verification solutions. We propose a verification methodology for in-car networks based on a system level test generator tool used for creating massive random biased stimuli, and on coverage and checking monitors. The test generator is an expert system based on an ontology of testing knowledge. A significant challenge is the continuous nature of the stimuli needed to represent the physical environment and the state of the internal components controlled by the vehicle's electronic systems. We report on applying our methodology to an example in-car network simulator.