Multidimensional context modeling applied to non-functional analysis of software

Context awareness is a first-class attribute of today software systems. Indeed, many applications need to be aware of their context in order to adapt their structure and behavior for offering the best quality of service even in case the software and hardware resources are limited. Modeling the context, its evolution, and its influence on the services provided by (possibly resource constrained) applications are becoming primary activities throughout the whole software life cycle, although it is still difficult to capture the multidimensional nature of context. We propose a framework for modeling and reasoning on the context and its evolution along multiple dimensions. Our approach enables (1) the representation of dependencies among heterogeneous context attributes through a formally defined semantics for attribute composition and (2) the stochastic analysis of context evolution. As a result, context can be part of a model-based software development process, and multidimensional context analysis can be used for different purposes, such as non-functional analysis. We demonstrate how certain types of analysis, not feasible with context-agnostic approaches, are enabled in our framework by explicitly representing the interplay between context evolution and non-functional attributes. Such analyses allow the identification of critical aspects or design errors that may not emerge without jointly taking into account multiple context attributes. The framework is shown at work on a case study in the eHealth domain.