Asymptotic Perturbation Bounds for Probabilistic Model Checking with Empirically Determined Probability Parameters
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9027862_2
- Type
- D - Journal article
- DOI
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10.1109/TSE.2015.2508444
- Title of journal
- IEEE Transactions on Software Engineering
- Article number
- -
- First page
- 623
- Volume
- 42
- Issue
- 7
- ISSN
- 0098-5589
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is significant in identifying discrepancies between a stochastic model used in probabilistic verification and the real-world system it represents when the model is built from statistical data. The issue was overlooked for many years in verification and this paper is the first to present a rigourous, mathematical formulation of the problem, with important steps towards a systematic solution. This study has impacted on further research in parameterised stochastic models (Baier et al at Dresden) as well as applications in event-streaming systems (Su at Wollongong) and engineer self-adaptive systems (Weyns et al at Leuven).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -