The uncertainty interaction problem in self-adaptive systems
Javier Cámara, Javier Troya, Antonio Vallecillo, Nelly Bencomo, Radu Calinescu, Betty H.C. Cheng,
David Garlan and
Bradley Schmerl.
In Software System and Modelling, August 2022. Expert Voice Paper (https://doi.org/10.1007/s10270-022-01037-6).
Online links: 
Abstract
| The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software
engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of
them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions)
in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different
uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in
subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties
from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym
expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with
respect to representing different types of uncertainty while capturing their interaction in models employed to reason about
self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken
from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research
in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling
towards holistic approaches that would enable the construction of more resilient self-adaptive systems. |
Keywords: Self-adaptation, uncertainty.
@Article{2022:SoSym:UIP,
AUTHOR = {C\'{a}mara, Javier and Troya, Javier and Vallecillo, Antonio and Bencomo, Nelly and Calinescu, Radu and Cheng, Betty H.C. and Garlan, David and Schmerl, Bradley},
TITLE = {The uncertainty interaction problem in self-adaptive systems},
YEAR = {2022},
MONTH = {August},
JOURNAL = {Software System and Modelling},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/Sosym2022.pdf},
ABSTRACT = {The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software
engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of
them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions)
in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different
uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in
subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties
from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym
expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with
respect to representing different types of uncertainty while capturing their interaction in models employed to reason about
self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken
from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research
in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling
towards holistic approaches that would enable the construction of more resilient self-adaptive systems.},
NOTE = { Expert Voice Paper ( https://doi.org/10.1007/s10270-022-01037-6)},
KEYWORDS = {Self-adaptation, uncertainty} } |
|
