Uncertainty propagation in Software-intensive systems

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Raffaela Mirandola (E-Mail: raffaela.mirandola@kit.edu)


Software-intensive systems are expected to operate properly under uncertain conditions, without interruption. Possible causes of uncertainty are complex interactions with human users, machine learning components, interdependencies between physical elements and software, and changing environmental conditions. Over the past decade, researchers have studied multiple strategies to understand and tame uncertainty. However, a comprehensive understanding of the precise nature of uncertainty is lacking. Besides, proposed solutions tend to deal with each type of uncertainty in isolation, without considering their possible propagation, interaction, and quantification.


The thesis aims to investigate the notion of uncertainty in a principled way, taking into account different types of uncertainty, their propagation in the system and their interaction. The main tasks will be:

  • Definition of techniques that consider explicit quantification and management of uncertainty propagation
  • Application of fuzzy methods and tools, Bayesian estimators and subjective logic


  • Insight into a highly relevant field of research
  • Opportunity to develop innovative technologie
  • Close connection to a current research project
  • Very good working environment and intensive support