Living with Uncertainty in the Age of Runtime Models

  • Holger Giese
  • Nelly Bencomo
  • Liliana Pasquale
  • Andres J. Ramirez
  • Paola Inverardi
  • Sebastian Wätzoldt
  • Siobhán Clarke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8378)


Uncertainty can be defined as the difference between information that is represented in an executing system and the information that is both measurable and available about the system at a certain point in its life-time. A software system can be exposed to multiple sources of uncertainty produced by, for example, ambiguous requirements and unpredictable execution environments. A runtime model is a dynamic knowledge base that abstracts useful information about the system, its operational context and the extent to which the system meets its stakeholders’ needs. A software system can successfully operate in multiple dynamic contexts by using runtime models that augment information available at design-time with information monitored at runtime. This chapter explores the role of runtime models as a means to cope with uncertainty. To this end, we introduce a well-suited terminology about models, runtime models and uncertainty and present a state-of-the-art summary on model-based techniques for addressing uncertainty both at development- and runtime. Using a case study about robot systems we discuss how current techniques and the MAPE-K loop can be used together to tackle uncertainty. Furthermore, we propose possible extensions of the MAPE-K loop architecture with runtime models to further handle uncertainty at runtime. The chapter concludes by identifying key challenges, and enabling technologies for using runtime models to address uncertainty, and also identifies closely related research communities that can foster ideas for resolving the challenges raised.


State Machine Robot System Epistemic Uncertainty Planning Step Runtime Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Holger Giese
    • 1
  • Nelly Bencomo
    • 2
  • Liliana Pasquale
    • 3
  • Andres J. Ramirez
    • 4
  • Paola Inverardi
    • 5
  • Sebastian Wätzoldt
    • 1
  • Siobhán Clarke
    • 6
  1. 1.Hasso Plattner Institute at the University of PotsdamGermany
  2. 2.Aston UniversityUK
  3. 3.Lero - Irish Software Engineering Research CentreIreland
  4. 4.Michigan State UniversityUSA
  5. 5.University of L’AquilaItaly
  6. 6.Trinity College DublinIreland

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