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Sanaz Sheikhi

Publications and source records attributed to Sanaz Sheikhi.

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Coverage Explorer: Coverage-guided Test Generation for Cyber Physical Systems

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.

eess.SY

The Black-Box Simplex Architecture for Runtime Assurance of Autonomous CPS

The Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) model-predictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands.

cs.SE