Towards Inverse Uncertainty Quantification in Software Development (Short Paper)

  • Matteo CamilliEmail author
  • Angelo Gargantini
  • Patrizia Scandurra
  • Carlo Bellettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10469)


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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Matteo Camilli
    • 1
    Email author
  • Angelo Gargantini
    • 2
  • Patrizia Scandurra
    • 2
  • Carlo Bellettini
    • 1
  1. 1.Department of Computer ScienceUniversità degli Studi di MilanoMilanItaly
  2. 2.Department of Management, Information and Production Engineering (DIGIP)Università degli Studi di BergamoBergamoItaly

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