Quantitative Timing Analysis of UML Activity Diagrams Using Statistical Model Checking

Gu, Fan; Zhang, Xinqian; Chen, Mingsong; Große, Daniel; Drechsler, Rolf

doi:10.3850/9783981537079_0339

Cited by 8 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al [18] present a way to evaluate the task allocation and scheduling strategies with time and power variation information. Gu et al [19] analyze quantitative timing of UML activity diagrams based on statistical model checking. Through using the build-in function random(), we can define a large set of commonly used distributions instead of the uniform and exponential distributions which UPPAAL-SMC supports.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

“…Contributing to the progress in CPSS analysis and verification frameworks, Gu et al [19] analyze the quantitative timing of UML activity diagrams via statistical models. Results provide a complete workflow for the stochastic modelling analysis of action executions.…”

Section: Related Workmentioning

confidence: 99%

“…The work presented in the current paper shares several common points with the aforementioned literature. For example, as in [19] and related studies, we also use statistical methods to analysis and verify the quantitative properties of CPSS.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

stohMCharts: A Modeling Framework for Quantitative Performance Evaluation of Cyber-Physical-Social Systems

Pan

Gao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Cyber-physical-social systems (CPSS) have recently gained attention from researchers due to their combination of cyber, physical, and social spaces. Modeling and Analysis of Real-Time and Embedded systems (MARTE) is a Unified Modeling Language (UML) extension profile that supports the specification, design, and verification of Real-Time Embedded Systems (RTES). While MARTE Statecharts can assist in describing CPS, it does not model the uncertainty within a CPSS environment. To enhance the accuracy of CPSS analysis, we propose the stohMCharts (stochastic hybrid MARTE statecharts) modelling framework as an extension of MARTE statecharts for modelling and analyzing stochastic hybrid systems. stohMCharts can model CPSS in a unified manner. Additionally, based on the mapping rules and algorithms, we have developed a tool to convert models built in stohMChart language into Networks Stochastic Hybrid Automata (NSHA) which can be verified by statistical model checker UPPAAL-SMC. We demonstrate the efficiency and accuracy of the framework by applying it to one autonomous driving scenarios.INDEX TERMS Cyber-physical-social systems (CPSS), modeling and analysis of real-time and embedded systems (MARTE), network of stochastic hybrid automata (NSHA), quantitative evaluation, automatic vehicles.

show abstract

Section: Statistical Model Checkingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%