Faster Statistical Model Checking by Means of Abstraction and Learning

Nouri, Ayoub; Raman, Balaji; Bozga, Marius; Legay, Axel; Bensalem, Saddek

doi:10.1007/978-3-319-11164-3_28

Cited by 26 publications

(17 citation statements)

References 26 publications

(33 reference statements)

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“…Moreover, for IoT systems of a larger size, there is no need for a BIP model to include all of the system's nodes; for the state‐space exploration we only need a model with representative instances of those types of nodes, which suffice to generate all relevant interleavings of events for the properties. For far more complex IoT applications and possible SMC scalability issues, the BIP model can be abstracted using automated stochastic abstraction techniques for identifying those events that are significant for the properties of interest.…”

Section: Discussionmentioning

confidence: 99%

Model‐based design of IoT systems with the BIP component framework

et al. 2018

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The design of software for networked systems with nodes running an Internet of things operating system faces important challenges due to the heterogeneity of interacting things and the constraints stemming from the often limited amount of available resources. In this context, it is hard to build confidence that a design solution fulfills the application's requirements. This paper introduces a design flow for web service applications of the representational state transfer style that is based on a formal modeling language, the behaviour, interaction, priority (BIP) component framework. The proposed flow applies the principles of separation of concerns in a component-based design process that supports the modular design and reuse of model artifacts. The BIP tools for state-space exploration allow verifying qualitative properties for service responsiveness, ie, the timely handling of events. Moreover, essential quantitative properties are validated through statistical model checking of a stochastic BIP model. All properties are preserved in actual implementation by ensuring that the deployed code is consistent with the validated model. We illustrate the design of a representational state transfer sense-compute-control application for a Wireless Personal Area Network architecture with nodes running the Contiki operating system. The results validate qualitative and quantitative properties for the system and include the study of error behaviours.

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Section: Discussionmentioning

confidence: 99%

Model‐based design of IoT systems with the BIP component framework

et al. 2018

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“…This may be a hindrance towards building the performance model. For this reason, we aim to investigate other model fitting techniques such as regression analysis [27] or learning Markov models [34]. In the future, we are also planning to continue exploring the HMAX case study from other perspectives such energy consumption and temperature.…”

Section: Discussionmentioning

confidence: 99%

Building faithful high-level models and performance evaluation of manycore embedded systems

Nouri

Bozga

Molnos³

et al. 2014

2014 Twelfth ACM/IEEE Conference on Formal Methods and Models for Codesign (MEMOCODE)

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Performance and functional correctness are key for successful design of modern embedded systems. Both aspects must be considered early in the design process to enable founded decision making towards final implementation. Nonetheless, building abstract system-level models that faithfully capture performance information along to functional behavior is a challenging task. In contrast to functional aspects, performance details are rarely available during early design phases and no clear method is known to characterize them. Moreover, once such system-level models are built they are inherently complex as they usually mix software models, hardware architecture constraints and environment abstractions. Their analysis by using traditional performance evaluation methods is reaching the limits and the need for more scalable and accurate techniques is becoming urgent. In this paper, we introduce a systematic method for building stochastic abstract performance models using statistical inference and model calibration and we propose statistical model checking as performance evaluation technique upon the obtained models. We experimented our method on a real-life case study. We were able to verify different timing properties.

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“…They apply automated propertyspecific abstraction/refinement to decrease the model-checking runtime. Nouri et al [39] also combine stochastic learning and abstraction with respect to some property. Their goal is to improve the runtime of SMC.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic black-box reachability checking (extended version)

Aichernig

Tappler

2019

Form Methods Syst Des

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Model checking has a long-standing tradition in software verification. Given a system design it checks whether desired properties are satisfied. Unlike testing, it cannot be applied in a black-box setting. To overcome this limitation Peled et al. introduced black-box checking, a combination of testing, model inference and model checking. The technique requires systems to be fully deterministic. For stochastic systems, statistical techniques are available. However, they cannot be applied to systems with non-deterministic choices. We present a black-box checking technique for stochastic systems that allows both, non-deterministic and probabilistic behaviour. It involves model inference, testing and probabilistic model-checking. Here, we consider reachability checking, i.e., we infer near-optimal input-selection strategies for bounded reachability.

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Faster Statistical Model Checking by Means of Abstraction and Learning

Cited by 26 publications

References 26 publications

Model‐based design of IoT systems with the BIP component framework

Model‐based design of IoT systems with the BIP component framework

Building faithful high-level models and performance evaluation of manycore embedded systems

Probabilistic black-box reachability checking (extended version)

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