Coverage Explorer: Coverage-guided Test Generation for Cyber Physical Systems
CoRR(2023)
摘要
Given the safety-critical functions of autonomous cyber-physical systems
(CPS) across diverse domains, testing these systems is essential. While
conventional software and hardware testing methodologies offer partial
insights, they frequently do not provide adequate coverage in a CPS. In this
study, we introduce a testing framework designed to systematically formulate
test cases, effectively exploring the state space of CPS. This framework
introduces a coverage-centric sampling technique, coupled with a cluster-based
methodology for training a surrogate model. The framework then uses model
predictive control within the surrogate model to generates test cases tailored
to CPS specifications. To evaluate the efficacy of the framework, we applied it
on several benchmarks, spanning from a kinematic car to systems like an
unmanned aircraft collision avoidance system (ACAS XU) and automatic
transmission system. Comparative analyses were conducted against alternative
test generation strategies, including randomized testing, as well as
falsification using S-TaLiRo.
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