RaM : Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning
- Submitting institution
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Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 44571239
- Type
- E - Conference contribution
- DOI
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10.1109/MODELS.2019.00005
- Title of conference / published proceedings
- Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems, MODELS 2019
- First page
- 216
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- 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
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1
- Research group(s)
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A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presented in a CORE rank A conference, this is the first paper to provide an architecture that ensures implementation of the causal connection between runtime models and the running system. The paper uses Bayesian learning to build and update the runtime models during execution to offer better informed decision-making based on runtime evidence. An extended version of the paper was invited for a special issue of the Journal SoSyM. The work provided the basis for the EPSRC project Twenty20Insight (EP/T017627/1 - 2020-2023, £586,520).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
