Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters

doi:10.1109/TSE.2015.2508444

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	Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters
	Su, Guoxin 1; Feng, Yuan 2,3; Chen, Taolue 4; Rosenblum, David S.1
	2016-07-01
发表期刊	IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
ISSN	0098-5589
卷号	42 期号:7 页码:623-639
摘要	Probabilistic model checking is a verification technique that has been the focus of intensive research for over a decade. One important issue with probabilistic model checking, which is crucial for its practical significance but is overlooked by the state-of-the-art largely, is the potential discrepancy between a stochastic model and the real-world system it represents when the model is built from statistical data. In the worst case, a tiny but nontrivial change to some model quantities might lead to misleading or even invalid verification results. To address this issue, in this paper, we present a mathematical characterization of the consequences of model perturbations on the verification distance. The formal model that we adopt is a parametric variant of discrete-time Markov chains equipped with a vector norm to measure the perturbation. Our main technical contributions include a closed-form formulation of asymptotic perturbation bounds, and computational methods for two arguably most useful forms of those bounds, namely linear bounds and quadratic bounds. We focus on verification of reachability properties but also address automata-based verification of omega-regular properties. We present the results of a selection of case studies that demonstrate that asymptotic perturbation bounds can accurately estimate maximum variations of verification results induced by model perturbations.
关键词	Asymptotic perturbation bound discrete-time Markov chain numerical iteration optimization parametric Markov chain perturbation analysis probabilistic model checking quadratic programming
DOI	10.1109/TSE.2015.2508444
语种	英语
资助项目	Singapore Ministry of Education[R-252-000-458-133] ; Australian Research Council[DP130102764] ; Australian Research Council[DP160101652] ; National Natural Science Foundation of China[61428208] ; National Natural Science Foundation of China[61502260] ; CAS/SAFEA International Partnership Program for Creative Research Team ; State Key Laboratory of Novel Software Technology at Nanjing University
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS记录号	WOS:000380053500002
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23254
专题	中国科学院数学与系统科学研究院
通讯作者	Su, Guoxin
作者单位	1.Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore 117548, Singapore 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, AMSS UTS Joint Res Lab Quantum Computat, Beijing 100864, Peoples R China 4.Middlesex Univ, Dept Comp Sci, London, England
推荐引用方式 GB/T 7714	Su, Guoxin,Feng, Yuan,Chen, Taolue,et al. Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters[J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING,2016,42(7):623-639.
APA	Su, Guoxin,Feng, Yuan,Chen, Taolue,&Rosenblum, David S..(2016).Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters.IEEE TRANSACTIONS ON SOFTWARE ENGINEERING,42(7),623-639.
MLA	Su, Guoxin,et al."Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters".IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 42.7(2016):623-639.