LLM-Based Property-Based Test Generation for Guardrailing Cyber-Physical SystemsShow others and affiliations
2026 (English)In: Lect. Notes Comput. Sci., Springer Nature , 2026, p. 18-46Conference paper, Published paper (Refereed)
Abstract [en]
Cyber-physical systems (CPSs) are complex systems that integrate physical, computational, and communication subsystems. The heterogeneous nature of these systems makes their safety assurance challenging. In this paper, we propose a novel automated approach for guardrailing cyber-physical systems using property-based tests (PBTs) generated by Large Language Models (LLMs). Our approach employs an LLM to extract properties from the code and documentation of CPSs. Next, we use the LLM to generate PBTs that verify the extracted properties on the CPS. The generated PBTs have two uses. First, they are used to test the CPS before it is deployed, i.e., at design time. Secondly, these PBTs can be used after deployment, i.e., at run time, to monitor the behavior of the system and guardrail it against unsafe states. We implement our approach in ChekProp and conduct preliminary experiments to evaluate the generated PBTs in terms of their relevance (how well they match manually crafted properties), executability (how many run with minimal manual modification), and effectiveness (coverage of the input space partitions). The results of our experiments and evaluation demonstrate a promising path forward for creating guardrails for CPSs using LLM-generated property-based tests.
Place, publisher, year, edition, pages
Springer Nature , 2026. p. 18-46
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 16220 LNCS
Keywords [en]
Cyber-Physical System, LLM4SE, Property-based Testing, Safety, Cyber Physical System, Embedded systems, Guard rails, Large scale systems, Safety testing, Communication subsystems, Cybe-physical systems, Cyber-physical systems, Language model, Model-based OPC, Property, Property-based, Test generations, Accident prevention
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-74252DOI: 10.1007/978-3-032-07132-3_3Scopus ID: 2-s2.0-105020252642ISBN: 9789819698936 (print)OAI: oai:DiVA.org:mdh-74252DiVA, id: diva2:2013372
Conference
Lecture Notes in Computer Science
2025-11-122025-11-122026-02-27Bibliographically approved