Asymptotic perturbation bounds for probabilistic model checking with empirically determined probability parameters
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1322
- Type
- D - Journal article
- DOI
-
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
- December
- Year of publication
- 2015
- URL
-
http://eprints.mdx.ac.uk/19192/
- Supplementary information
-
-
- 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
-
3
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Probabilistic model checking is a relevant and novel verification technique. However, it suffers from the discrepancy between the stochastic model and the real-world system it represents. This work formalized the consequences of model perturbations on the verification process. The formalization combines Markov chains and a vector norm to measure the perturbation. It is significant because it provides a formulation of perturbation bounds, and methods for two useful bounds: linear and quadratic. It verifies reachability properties and addresses automata-based verification. It demonstrates that asymptotic perturbation bounds can accurately estimate maximum variations of verification results induced by the perturbations.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -