Living with uncertainty in the age of runtime models

Holger Giese, Nelly Bencomo, Liliana Pasquale, Andres J. Ramirez, Paola Inverardi, Sebastian Wätzoldt, Siobhán Clarke

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

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.

Original languageEnglish
Title of host publicationModels@run.time
Subtitle of host publicationfoundations, applications, and roadmaps
EditorsNelly Bencomo, Robert France, Betty H.C. Cheng, Uwe Aßmann
Place of PublicationCham (CH)
PublisherSpringer
Pages47-100
Number of pages54
ISBN (Electronic)978-3-319-08915-7
ISBN (Print)978-3-319-08914-0
DOIs
Publication statusPublished - 31 Dec 2014
EventDagstuhl Seminar 11481 on Models@run.time - Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, Wadern, Germany
Duration: 27 Nov 20112 Dec 2011

Publication series

NameLecture Notes in computer science
PublisherSpringer
Volume8378
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceDagstuhl Seminar 11481 on Models@run.time
CountryGermany
CityWadern
Period27/11/112/12/11

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  • Research Output

    • 1 Scholarly edition
    • 1 Foreword/postscript

    Models@run.time: foundations, applications, and roadmaps

    Bencomo, N. (ed.), France, R. (ed.), Cheng, B. H. C. (ed.) & Aßmann, U. (ed.), 31 Dec 2014, Cham (CH): Springer. 319 p. (Lecture notes in computer science; vol. 8378)

    Research output: Book/ReportScholarly edition

  • Preface

    Bencomo, N., France, R., Cheng, B. H. C. & Aßmann, U., 2014, Models@run.time: foundations, applications, and roadmaps. Bencomo, N., France, R., Cheng, B. H. C. & Aßmann, U. (eds.). Cham (CH): Springer, p. v-viii 4 p. (Lecture Notes in Computer Science; vol. 8378).

    Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

    Cite this

    Giese, H., Bencomo, N., Pasquale, L., Ramirez, A. J., Inverardi, P., Wätzoldt, S., & Clarke, S. (2014). Living with uncertainty in the age of runtime models. In N. Bencomo, R. France, B. H. C. Cheng, & U. Aßmann (Eds.), Models@run.time: foundations, applications, and roadmaps (pp. 47-100). (Lecture Notes in computer science; Vol. 8378). Springer. https://doi.org/10.1007/978-3-319-08915-7_3