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.
This is a short paper accepted in the new ideas and work-in-progress section of SEFM 2017.
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Camilli, M., Gargantini, A., Scandurra, P., Bellettini, C. (2017). Towards Inverse Uncertainty Quantification in Software Development (Short Paper). In: Cimatti, A., Sirjani, M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science(), vol 10469. Springer, Cham. https://doi.org/10.1007/978-3-319-66197-1_24
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DOI: https://doi.org/10.1007/978-3-319-66197-1_24
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