1,380 research outputs found
A probabilistic extension of UML statecharts: specification and verification
This paper is the extended technical report that corresponds to a published paper [14]. This paper introduces means to specify system randomness within UML statecharts, and to verify probabilistic temporal properties over such enhanced statecharts which we call probabilistic UML statecharts. To achieve this, we develop a general recipe to extend a statechart semantics with discrete probability distributions, resulting in Markov decision processes as semantic models. We apply this recipe to the requirements-level UML semantics of [8]. Properties of interest for probabilistic statecharts are expressed in PCTL, a probabilistic variant of CTL for processes that exhibit both non-determinism and probabilities. Verification is performed using the model checker Prism. A model checking example shows the feasibility of the suggested approach
Dependability checking with StoCharts: Is train radio reliable enough for trains?
Performance, dependability and quality of service (QoS) are prime aspects of the UML modelling domain. To capture these aspects effectively in the design phase, we have recently proposed STOCHARTS, a conservative extension of UML statechart diagrams. In this paper, we apply the STOCHART formalism to a safety critical design problem. We model a part of the European Train Control System specification, focusing on the risks of wireless communication failures in future high-speed cross-European trains. Stochastic model checking with the model checker PROVER enables us to derive constraints under which the central quality requirements are satisfied by the STOCHART model. The paper illustrates the flexibility and maturity of STOCHARTS to model real problems in safety critical system design
A comparative reliability analysis of ETCS train radio communications
StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and were applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Mƶbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study
From StoCharts to MoDeST: a comparative reliability analysis of train radio communications
StoCharts have been proposed as a UML statechart extension for performance and dependability evaluation, and have been applied in the context of train radio reliability assessment to show the principal tractability of realistic cases with this approach. In this paper, we extend on this bare feasibility result in two important directions. First, we sketch the cornerstones of a mechanizable translation of StoCharts to MoDeST. The latter is a process algebra-based formalism supported by the Motor/Mƶbius tool tandem. Second, we exploit this translation for a detailed analysis of the train radio case study
A Holistic Approach in Embedded System Development
We present pState, a tool for developing "complex" embedded systems by
integrating validation into the design process. The goal is to reduce
validation time. To this end, qualitative and quantitative properties are
specified in system models expressed as pCharts, an extended version of
hierarchical state machines. These properties are specified in an intuitive way
such that they can be written by engineers who are domain experts, without
needing to be familiar with temporal logic. From the system model, executable
code that preserves the verified properties is generated. The design is
documented on the model and the documentation is passed as comments into the
generated code. On the series of examples we illustrate how models and
properties are specified using pState.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338
Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae
Diabetes in pregnancy (DIP) is an increasing public health priority in the
Australian Capital Territory, particularly due to its impact on risk for
developing Type 2 diabetes. While earlier diagnostic screening results in
greater capacity for early detection and treatment, such benefits must be
balanced with the greater demands this imposes on public health services. To
address such planning challenges, a multi-scale hybrid simulation model of DIP
was built to explore the interaction of risk factors and capture the dynamics
underlying the development of DIP. The impact of interventions on health
outcomes at the physiological, health service and population level is measured.
Of particular central significance in the model is a compartmental model
representing the underlying physiological regulation of glycemic status based
on beta-cell dynamics and insulin resistance. The model also simulated the
dynamics of continuous BMI evolution, glycemic status change during pregnancy
and diabetes classification driven by the individual-level physiological model.
We further modeled public health service pathways providing diagnosis and care
for DIP to explore the optimization of resource use during service delivery.
The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201
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