Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking
Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking
复制标题
使用统计模型检查对不确定性感知混合 AADL 设计进行定量性能评估
DOI：
10.1109/tcad.2017.2681076
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发表时间：
2017-03
影响因子：
2.9
通讯作者：
Tingliang Zhou
中科院分区：
文献类型：
--
作者：
Yongxiang Bao;陈铭松;Qi Zhu;Tongquan Wei;Frederic Mallet;Tingliang Zhou
混合架构分析与设计语言（AADL）已被提出用于对嵌入式控制系统和连续物理环境之间的交互进行建模。然而，混合AADL设计的最坏情况性能分析往往导致……
The hybrid architecture analysis and design language (AADL) has been proposed to model the interactions between embedded control systems and continuous physical environment. However, the worst-case performance analysis of hybrid AADL designs often leads to overly pessimistic estimations, and is not suitable for accurate reasoning about overall system performance, in particular when the system closely interacts with an uncertain external environment. To address this challenge, this paper proposes a statistical model checking-based framework that can perform quantitative evaluation of uncertainty-aware hybrid AADL designs against various performance queries. Our approach extends hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers’ requirements into networks of priced timed automata and performance queries, respectively. Comprehensive experimental results on the movement authority scenario of Chinese train control system level 3 demonstrate the effectiveness of our approach.
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DOI：
10.1016/b978-044450813-3/50026-6
发表时间：
2001
期刊：
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影响因子：
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作者：
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通讯作者：
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2014-11
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期刊：
The American Statistician
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