Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6539231
- Type
- D - Journal article
- DOI
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10.1109/TSE.2018.2886898
- Title of journal
- IEEE Transactions on Software Engineering
- Article number
- -
- First page
- 189
- Volume
- 47
- Issue
- 1
- ISSN
- 0098-5589
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed LAR approach has been applied and evaluated in several other studies. As a case study, LAR has been adapted to learn models of a complex real-world Secure Water Treatment System (SWaT: https://itrust.sutd.edu.sg/testbeds/secure-water-treatment-swat/) and verify the models against probabilistic safety properties in FM2018 (LNCS 10951, pages 73-92). The resultant models were also used for evaluating an active learning algorithm of Markov Chains in ICFEM17 (LNCS 10610, pages 379-395), and a sampling algorithm of Interval Markov Chains in IEEE DSN2018 (10.1109/DSN.2018.00040).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -