Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review

Pappagallo, Angela; Massini, Annalisa; Tronci, Enrico

doi:10.3390/info11120588

Cited by 10 publications

(3 citation statements)

References 79 publications

(104 reference statements)

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“…They typically start from a behavioural model of the system behaviour (e.g. hybrid automata [20,52] or Simulink [1]) and, then, check whether the model complies with behavioural requirements expressed in some logic (temporal [54,71] or hybrid [7,13,33,47,53]). These methods require engineers to have already decided on a system design and ignore the numerous possible design alternatives that exist.…”

Section: Behavioural Analysis To Support Post-cd Design Stagesmentioning

confidence: 99%

Variability-Aware Design of Space Systems: Variability Modelling, Configuration Workflow and Research Directions

Lazreg

Bohlachov

Rana

et al. 2022

Proceedings of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems

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Designing spacecraft such as satellites is known as an extremely difficult problem. It requires complex decision making at different concerns from mission requirements to system architectures. Such complexity might lead to design system architectures that are not feasible in practice or do not fulfil the requirements. Moreover, the lack of automation to assess high-level designs reduce the benefits of the early design phases by missing more suitable designs and increasing the design time.In this paper we discuss potential research directions and propose a potential framework that aims to i) drive the engineers in the different design steps ii) capture the feasible system architectures regarding the requirements and constraints, iii) refine and assess selected architectures through simulations. Our framework is model-driven and built on two complementary modules. The first module is a configurator based on attributed features models that support engineers in the design process of system architectures. Second, a simulation engine refines and assess automatically the selected system architectures with respect to the requirements. CCS CONCEPTS• Computing methodologies → Model development and analysis; • Applied computing → Aerospace.

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Section: Behavioural Analysis To Support Post-cd Design Stagesmentioning

confidence: 99%

Variability-Aware Design of Space Systems: Variability Modelling, Configuration Workflow and Research Directions

Lazreg

Bohlachov

Rana

et al. 2022

Proceedings of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems

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“…In the spirit of using randomness for exploration, statistical model-checking SMC, which is based on sampling behaviors of a system model and using statistical inference to estimate the probability that the system satisfies a property, has been applied for CPS (see some recent surveys [25,26,2,27,35] which include descriptions of the existing SMC tools). Unlike the concern of this approach involving rare events which is addressed by techniques such as importance sampling, cross-entropy methods, trace uniformity requirements have not been explored.…”

Section: Related Workmentioning

confidence: 99%

Sampling of shape expressions with ShapEx

Basset

Dang

Gigler

et al. 2021

Proceedings of the 19th ACM-IEEE International Conference on Formal Methods and Models for System Design

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Cyber-physical systems (CPS) are increasingly becoming driven by data, using multiple types of sensors to capture huge amounts of data. Extraction and characterization of useful information from big streams of data is a challenging problem. Shape expressions facilitate formal specification of rich temporal patterns encountered in time series as well as in behaviors of CPS. In this paper, we introduce a method for systematically sampling shape expressions. The proposed approach combines methods for uniform sampling of automata (for exploring qualitative shapes) with hit-and-run Monte Carlo sampling procedures (for exploring multi-dimensional parameter spaces defined by sets of possibly non-linear constraints). We study and implement several possible solutions and evaluate them in the context of visualisation and testing applications.

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“…The generated environment model is then used to simulate the CPS software controller as often as needed. Using the simulation, engineers can efficiently assess the CPS goal achievement with well-known verification methods, such as statistical model checking [31] and model-based testing [3].…”

Section: Introductionmentioning

confidence: 99%

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

Shin,

Bae

2024

ACM Trans. Embed. Comput. Syst.

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Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop. In this paper, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the paper.

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Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review

Cited by 10 publications

References 79 publications

Variability-Aware Design of Space Systems: Variability Modelling, Configuration Workflow and Research Directions

Variability-Aware Design of Space Systems: Variability Modelling, Configuration Workflow and Research Directions

Sampling of shape expressions with ShapEx

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

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