Abstract
With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification
that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification
(i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model.
We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning
and online Model-based testing.
[download the pdf file] [DOI]