Coverage-guided State Space Exploration of Autonomous Cyber-Physical Systems

Autonomous Cyber-Physical Systems (CPS) play a substantial role in many domains, such as aerospace, transportation, critical infrastructure, and industrial manufacturing. However, despite the popularity of autonomous CPS, their susceptibility to errant behavior is a considerable concern for safety-critical applications. Testing and simulation is the most common method used in practice to ensure the correctness of autonomous CPS due to their ability to scale to complex systems. In many domains, CPS complexity and scalability have been exponentially growing and will continue to expand due to rapid integration with machine learning components and rising autonomy level such as unmanned aerial vehicles or self-driving cars. Traditional software test methodologies which extensively depend on code coverage are expensive, require code instrumentation, and are ineffective in verifying CPS behavior. Moreover, these test methodologies suffer from lack of flexibility where dynamical CPS control requirements and plant parameters are evolving through continuous state space and time. We investigate ways to improve automated test case generation for autonomous CPS using coverage-guided state space exploration, which systematically generates trajectories to explore desired (or undesired) outcomes. We introduce a novel coverage metric notion and integrate this metric with various techniques, such as fuzz testing or model predictive control (MPC), to generate test cases
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