Towards Inverse Uncertainty Quantification in Software Development (Short Paper) [chapter]

Matteo Camilli, Angelo Gargantini, Patrizia Scandurra, Carlo Bellettini
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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
more &raquo; ... ing and online Model-based testing. This is a short paper accepted in the new ideas and work-in-progress section of SEFM 2017.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-66197-1_24">doi:10.1007/978-3-319-66197-1_24</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rqb5pxterbagtlpcgwz6v3mray">fatcat:rqb5pxterbagtlpcgwz6v3mray</a> </span>
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