Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Making Resource Decisions for Software Projects
Proceedings of the 26th International Conference on Software Engineering
Reasoning about partial goal satisfaction for requirements and design engineering
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Goal-Based Modeling of Dynamically Adaptive System Requirements
ECBS '08 Proceedings of the 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
RELAX: a language to address uncertainty in self-adaptive systems requirement
Requirements Engineering - RE'09 Special Issue; Guest Editor:Kevin T Ryan
Software engineering in an uncertain world
Proceedings of the FSE/SDP workshop on Future of software engineering research
Requirements-Aware Systems: A Research Agenda for RE for Self-adaptive Systems
RE '10 Proceedings of the 2010 18th IEEE International Requirements Engineering Conference
Taming uncertainty in self-adaptive software
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Using Dynamic Decision Networks and Extended Fault Trees for Autonomous FDIR
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Representing and reasoning about preferences in requirements engineering
Requirements Engineering - Special Issue on Best Papers of RE'10: Requirements Engineering in a Multi-faceted World
A formal approach to adaptive software: continuous assurance of non-functional requirements
Formal Aspects of Computing
Towards requirements aware systems: Run-time resolution of design-time assumptions
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Self-Explanation in Adaptive Systems
ICECCS '12 Proceedings of the 2012 IEEE 17th International Conference on Engineering of Complex Computer Systems
Dealing with uncertainty in early software architecture
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Relaxing claims: coping with uncertainty while evaluating assumptions at run time
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Supporting decision-making for self-adaptive systems: from goal models to dynamic decision networks
REFSQ'13 Proceedings of the 19th international conference on Requirements Engineering: Foundation for Software Quality
Uncertainty handling in goal-driven self-optimization - Limiting the negative effect on adaptation
Journal of Systems and Software
| Hi-index | 0.00 |
Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.