sefm2017 Matteo Camilli Angelo Gargantini Patrizia Scandurra Carlo Bellettini

Towards Inverse Uncertainty Quantification in Software Development

in International Conference on Software Engineering and Formal MethodsSpringer International Publishing (2017): 375--381

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]

My sw links