KMS Of Academy of mathematics and systems sciences, CAS
| Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters | |
| Su, Guoxin1; Feng, Yuan2,3; Chen, Taolue4; Rosenblum, David S.1 | |
| 2016-07-01 | |
| Source Publication | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
 |
| ISSN | 0098-5589 |
| Volume | 42Issue:7Pages:623-639 |
| Abstract | 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. |
| Keyword | 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 |
| Language | 英语 |
| Funding Project | 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 Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Software Engineering ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000380053500002 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/23254 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Su, Guoxin |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment