A Statistical Approach for Timed Reachability in AADL Models

Bruintjes, Harold; Katoen, Joost-Pieter; Lesens, David

doi:10.1109/dsn.2015.32

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Nevertheless, there is interesting research that deals with stochastic properties and statistical model checking for the analysis of extended AADL models. One such example is in the work of Bruintjes et al [47], where the authors have used the SMC approach for timed reachability analysis of extended AADL designs. Although our approach also focuses on linear systems, it is different from the mentioned work in the fact that we focus on abstract components, and also introduce BA modeling for capturing the functional behavior of our modules, specifically for modeling the behavior of intelligent DSS.…”

Section: Related Workmentioning

confidence: 99%

A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions

Kunnappilly

Marinescu

Seceleanu

2019

Sensors

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Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that support the seamless design of the integrated assisted-living functions, as well as capabilities for the formal modeling and analysis of the architecture. In this paper, we attempt to address this need, by proposing a generic integrated ambient assisted living system architecture, consisting of sensors, data collection, local and cloud processing schemes, and an intelligent decision support system, which can be easily extended to suit specific architecture categories. Our solution is customizable, therefore, we show three instantiations of the generic model, as simple, intermediate, and complex configurations, respectively, and show how to analyze the first and third categories by model checking. Our approach starts by specifying the architecture, using an architecture description language, in our case, the Architecture Analysis and Design Language, which can also account for the probabilistic behavior of such systems, and captures the possibility of component failure. To enable formal analysis, we describe the semantics of the simple and complex architectures within the framework of timed automata. We show that the simple architecture is amenable to exhaustive model checking by employing the UPPAAL tool, whereas for the complex architecture we resort to statistical model checking for scalability reasons. In this case, we apply the statistical extension of UPPAAL, namely UPPAAL SMC. Our work paves the way for the development of formally assured future ambient assisted living solutions.

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Section: Related Workmentioning

confidence: 99%

A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions

Kunnappilly

Marinescu

Seceleanu

2019

Sensors

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“…The analyses are supported by the following verification engines: nuXmv [9] for correctness checking; OCRA [10] for contract-based analysis; IMCA [19] and MRMC [20] for performance analysis by probabilistic model checking; slimsim [8] for statistical model checking and xSAP [1] for safety analysis.…”

Section: Toolset Overviewmentioning

confidence: 99%

“…However, neither approach supports hybrid models containing clocks. For the analysis of these models, statistical model checking techniques [7,8] are employed, which use Monte-Carlo simulation to determine, within a certain margin of likelihood and error, the probability of quantitative properties.…”

Section: New Functionalities In Compass 30mentioning

confidence: 99%

“…A second case study followed during the Critical Design Review stage, focusing on modeling practices and diagnosability [4], with a scale twice the size of [17]. A smaller scale case study was later performed as part of the HASDEL project [8]. Recently, the CubETH nano-satellite was represented as a model with 82 components and analyzed using COMPASS 3.0 [7].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

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COMPASS 3.0

Bozzano

Bruintjes

Cimatti

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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COMPASS (COrrectness, Modeling and Performance of AeroSpace Systems) is an international research effort aiming to ensure system-level correctness, safety, dependability and performability of onboard computer-based aerospace systems. In this paper we present COMPASS 3.0, which brings together the results of various development projects since the original inception of COMPASS. Improvements have been made both to the frontend, supporting an updated modeling language and user interface, as well as to the backend, by adding new functionalities and improving the existing ones. New features include Timed Failure Propagation Graphs, contract-based analysis, hierarchical fault tree generation, probabilistic analysis of non-deterministic models and statistical model checking.

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