Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification

Zuliani, Paolo; Platzer, André; Clarke, Edmund M.

doi:10.21236/ada531406

Cited by 90 publications

(146 citation statements)

References 36 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…The simulation model for this use case uses statistical model checking by Bayesian Interval Estimates [45].…”

Section: Use Case 1: the Multi-room Heating Systemmentioning

confidence: 99%

“…To overcome this problem, we have to use the statistical model checking (SMC) approach based on Bayesian statistics [44,45]. SMC is a verification method that provides statistical evidence to check whether a stochastic system satisfies a wide range of temporal properties with a certain probability and confidence level or not.…”

Section: • Requirement 1: Ability To Elastically Execute Multiplementioning

confidence: 99%

“…In use case 1 described in Section 3.1.1, the user provides the approximate number of simulations needed for stochastic model checking as an input to attain the desired confidence level for the output [45]. For the second use case described in Section 3.1.2, the number of simulations is a user input.…”

Section: Evaluating Simaas For Meeting Deadlines and Resource Consumpmentioning

confidence: 99%

See 2 more Smart Citations

A simulation as a service cloud middleware

et al. 2015

View full text Add to dashboard Cite

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloudbased Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.

show abstract

“…The simulation model for this use case uses statistical model checking by Bayesian Interval Estimates [45].…”

Section: Use Case 1: the Multi-room Heating Systemmentioning

confidence: 99%

Section: • Requirement 1: Ability To Elastically Execute Multiplementioning

confidence: 99%

Section: Evaluating Simaas For Meeting Deadlines and Resource Consumpmentioning

confidence: 99%

See 1 more Smart Citation

A simulation as a service cloud middleware

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Also, additional information, for example, in terms of associated costs, can be incorporated [16]. Instead of hypothesis testing, one can also impose a prior distribution on the probability of satisfaction and update a Bayesian belief sequentially as new samples are drawn from the generative model [17]. Most approaches are only applicable, if no decisions are needed to be resolved other than random nondeterministic ones, however, see [18] for a recent extension to Markov Decision Processes.…”

Section: Model Checking Of Stochastic Hybrid Automata: Related Workmentioning

confidence: 99%

Statistical model checking for stochastic hybrid systems involving nondeterminism over continuous domains

Ellen

Gerwinn

Fränzle

2014

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

Behavioral verification of technical systems invol ving both discrete and continuous components is a common and demanding task. The behavior of such systems can often be characterized using stochastic hybrid automata, leading to verification problems which can be formalized and solved using stochastic logic calculi such as stochastic satisfiability modulo theory (SSMT). While algorithms for discharging proof obligations in SSMT form exist, their applicability is limited due to the computational complexity, which often increases exponentially with the number of quantified variables. Recently, statistical model checking has been successfully applied to stochastic hybrid systems, thereby increasing the size of the system for which verification problems is tractable. However, being based on randomized simulation, these methods usually cannot handle non-determinism. In previous work, we have deviated from the usual approach of simulating the model and rather proposed a statistical method for SSMT solving which, being based on statistical AI planning algorithms, can also treat non-determinism over a finite domain. Here, we extend this previous work to the case of continuous domains. In particular, using ideas from noisy optimization, we adaptively build up a decision tree recording the findings and guiding further exploration, thereby favoring the currently most promising sub-domain. The non-determinism is resolved by translating the satisfac-C. Ellen (B) · S. Gerwinn Transportation, tion problem into an optimization problem, thereby computing both optimistic and pessimistic bounds on the probability of satisfaction. At each stage of the evaluation process, we show how to obtain confidence statements about the probability of satisfaction for the overall SSMT formula, including reliable estimates on the optimal resolution of any nondeterministic choice involved.

show abstract

“…In the past, to address this challenge, Statistical Model Checking (SMC) for SCPS was proposed [28], [9]. In brief, * The authors equally contributed to the work.…”

Section: Introductionmentioning

confidence: 99%

Robustness-guided temporal logic testing and verification for Stochastic Cyber-Physical Systems

Abbas

Hoxha

Fainekos

et al. 2014

The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent

View full text Add to dashboard Cite

We present a framework for automatic specificationguided testing for Stochastic Cyber-Physical Systems (SCPS). The framework utilizes the theory of robustness of Metric Temporal Logic (MTL) specifications to quantify how robustly an SCPS satisfies a specification in MTL. The goal of the testing framework is to detect system operating conditions that cause the system to exhibit the worst expected specification robustness. The resulting expected robustness minimization problem is solved using Markov chain Monte Carlo algorithms. This also allows us to use finite-time guarantees, which quantify the quality of the solution after a finite number of simulations. In a Model-Based Design (MBD) process, our framework can be combined with Statistical Model Checking (SMC). Finally, we present a case study on a high fidelity engine model where the goal is to verify the air-to-fuel ratio problem.

show abstract

Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification

Cited by 90 publications

References 36 publications

A simulation as a service cloud middleware

A simulation as a service cloud middleware

Statistical model checking for stochastic hybrid systems involving nondeterminism over continuous domains

Robustness-guided temporal logic testing and verification for Stochastic Cyber-Physical Systems

Contact Info

Product

Resources

About