Model Driven Software Reconfiguration by Exploiting Grammar Based Genetic Programming

Dynamic reconfiguration of software systems can be achieved by exploiting variability models of such systems, combined with mechanisms for selecting and instantiating the appropriate system configuration. We developed a model-driven approach for dynamic software reconfiguration, which uses a component profile-oriented feature model for representing different configurations of a software system, and a grammar based genetic programming tool that, at run-time, automatically generates an optimal system configuration. The resulting feature configuration is transformed to a target format such as JSON, SQL or other specification that allows us to instantiate the new system configuration. In this paper we focus on the run-time reasoning and propagation aspect, and discuss our experience on applying the approach to a use case