Dynamic decision networks for decision-making in self-adaptive systems:a case study

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 speci?cally 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, speci?cally 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 bene?ts 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.

Design software architecture models using ontology

Software architecture plays an essential role in the high level description of a system design, where the structure and communication are emphasized. Despite its importance in the software engineering process, the lack of formal description and automated verification hinders the development of good software architecture models. In this paper, we present an approach to support the rigorous design and verification of software architecture models using the semantic web technology. We view software architecture models as ontology representations, where their structures and communication constraints are captured by the Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). Specific configurations on the design are represented as concrete instances of the ontology, to which their structures and dynamic behaviors must conform. Furthermore, ontology reasoning tools can be applied to perform various automated verification on the design to ensure correctness, such as consistency checking, style recognition, and behavioral inference.