This paper introduces Shelley, a novel model checking framework used to verify the order of function calls, developed in the context of Cyber-Physical Systems (CPS). Shelley infers the model directly from MicroPython code, so as to simplify the process of checking requirements expressed in a temporal logic. Applications for CPS need to reason about the end of execution to verify the reclamation/release of physical resources, so our temporal logic is stated on finite traces. Lastly, Shelley infers the behavior from code using an inter-procedural and compositional analysis, thus supporting the usual object-oriented programming techniques employed in MicroPython code. To evaluate our work, we present an experience report on an industrial application and evaluate the bounds of the validity checks (up to subsystems under 10 s on a desktop computer).
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This content will become publicly available on May 13, 2025
Poster Abstract: Assuring LLM-Enabled Cyber-Physical Systems
Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. The autonomy and self-adaptation capabilities of CPS mark a significant evolution from traditional control systems. Machine learning significantly enhances the functionality and efficiency of Cyber-Physical Systems (CPS). Large Language Models (LLM), like GPT-4, can augment CPS’s functionality to a new level
by providing advanced intelligence support. This fact makes the applications above potentially unsafe and thus untrustworthy if deployed to the real world. We propose a comprehensive and general assurance framework for LLM-enabled CPS. The framework consists of three modules: (i) the context grounding module assures the task context has been accurately grounded (ii) the temporal Logic requirements specification module forms the temporal requirements into logic specifications for
prompting and further verification (iii) the formal verification module verifies the output of the LLM and provides feedback as a guideline for LLM. The three modules execute iteratively until the output of LLM is verified. Experiment results demonstrate that our assurance framework can assure the LLM-enabled CPS.
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- Award ID(s):
- 2333980
- PAR ID:
- 10499419
- Publisher / Repository:
- ACM/IEEE
- Date Published:
- Journal Name:
- ACM/IEEE International Conference on Cyber-Physical Systems
- Format(s):
- Medium: X
- Location:
- Hong Kong, China
- Sponsoring Org:
- National Science Foundation
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