In recent years, the development of automated and autonomously operating cyber-physical systems such as cars or drones is on the rise. Testing the safe behavior of these complex systems in all relevant situations is a challenge that is not easily solved.
Due to the high amount of needed test samples, the verification and validation of cyber-physical systems only with real-world testing is infeasible. To complement these efforts, we propose the use of scenario-based testing. In this method, the cyber-physical system is tested in different scenario types, which describe situations in which we aim to test the system's behavior. For cars, examples of these scenario types are "Lane Change" or "Cut-In". Each of these scenario types includes parameters to describe its various aspects, e.g., starting position of the system under test. When generating test cases for these scenario types, concrete values are assigned to all parameters of the considered scenario type.
Since it is not feasible to test the cyber-physical system in all possible scenario instances of one scenario type, we need to select "good" test cases that challenge the system under test. A test case is a "good" test case if it can reveal potential faults in the system. When generating these "good" test cases, different optimization techniques such as search-based techniques can be used.
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