An Approach for Managing Quality Attributes at Runtime using Feature Models

Feature modeling has been widely used in domain engineering for the development and configuration of software products. A feature model represents the set of possible configurations to apply in a given context. Recently, this formalism was applied to the runtime (re-) configuration of systems with high variability and context changes, in which the selection of the best candidate configuration is seen as an optimization problem based on quality criteria. To this end, we propose an approach for the specification, measurement and optimization of runtime quality attributes based on feature models, and furthermore, we describe its integration into a component-based architecture for supporting dynamically adaptive systems. Feature models are annotated with quality-attribute properties and metrics, and then an efficient and flexible algorithm is used to deal with the optimization problem. We report on some examples of adaptation and quality-attribute scenarios in the context of a video surveillance domain, in order to illustrate the pros and cons of our approach.