research-article

Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes

Authors:

Lijun ZhangAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 25, Issue 3

Article No.: 21, Pages 1 - 39

https://doi.org/10.1145/2907943

Published: 06 June 2016 Publication History

Abstract

Probabilistic models are widely deployed in various systems. To ensure their correctness, verification techniques have been developed to analyze probabilistic systems. We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process. Different from previous works, weighted assumptions are introduced to attain completeness of our framework. Since weighted assumptions can be implicitly represented by multiterminal binary decision diagrams (MTBDDs), we give an >i<L>/i<*-based learning algorithm for MTBDDs to infer weighted assumptions. Experimental results suggest promising outlooks for our compositional technique.

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Liu YMa YYang Y(2022)A three-valued model abstraction framework for PCTL* stochastic model checkingAutomated Software Engineering10.1007/s10515-022-00327-z29:1Online publication date: 2-Mar-2022
https://dl.acm.org/doi/10.1007/s10515-022-00327-z
Wang FCao ZTan LZong H(2020)Survey on learning-based formal methods: Taxonomy, Applications and Possible future directionsIEEE Access10.1109/ACCESS.2020.3000907(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3000907
Tran HHung PNguyen VAoki T(2020)A framework for assume-guarantee regression verification of evolving softwareScience of Computer Programming10.1016/j.scico.2020.102439193(102439)Online publication date: Jul-2020
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Index Terms

Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Formal software verification

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 25, Issue 3

August 2016

291 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/2943790

Editor:
David S. Rosenblum
National University of Singapore, Singapore

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Copyright © 2016 ACM.

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Publication History

Published: 06 June 2016

Accepted: 01 March 2016

Revised: 01 December 2015

Received: 01 August 2015

Published in TOSEM Volume 25, Issue 3

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CAS/SAFEA International Partnership Program for Creative Research Teams
Ministry of Science and Technology of Taiwan
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Cited By

Liu YMa YYang Y(2022)A three-valued model abstraction framework for PCTL* stochastic model checkingAutomated Software Engineering10.1007/s10515-022-00327-z29:1Online publication date: 2-Mar-2022
https://dl.acm.org/doi/10.1007/s10515-022-00327-z
Wang FCao ZTan LZong H(2020)Survey on learning-based formal methods: Taxonomy, Applications and Possible future directionsIEEE Access10.1109/ACCESS.2020.3000907(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3000907
Tran HHung PNguyen VAoki T(2020)A framework for assume-guarantee regression verification of evolving softwareScience of Computer Programming10.1016/j.scico.2020.102439193(102439)Online publication date: Jul-2020
https://doi.org/10.1016/j.scico.2020.102439
Li RLiu Y(2019)Compositional Stochastic Model Checking Probabilistic Automata via Symmetric Assume-Guarantee Rule2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA.2019.8886808(110-115)Online publication date: May-2019
https://doi.org/10.1109/SERA.2019.8886808
Ma YCao ZLiu Y(2019)Genetic Algorithm-Based Assume-Guarantee Reasoning for Stochastic Model Checking2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA.2019.8886798(124-127)Online publication date: May-2019
https://doi.org/10.1109/SERA.2019.8886798
Ma YCao ZLiu Y(2019)A Probabilistic Assume-Guarantee Reasoning Framework Based on Genetic AlgorithmIEEE Access10.1109/ACCESS.2019.29246397(83839-83851)Online publication date: 2019
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Bouchekir RBoukala M(2018)Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov processInnovations in Systems and Software Engineering10.1007/s11334-018-0316-714:3(229-244)Online publication date: 1-Sep-2018
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Bouchekir RBoukala M(2018)Toward Implicit Learning for the Compositional Verification of Markov Decision ProcessesVerification and Evaluation of Computer and Communication Systems10.1007/978-3-030-00359-3_13(200-217)Online publication date: 31-Aug-2018
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