Software Engineering for Self-Adaptive Systems
Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening
Journal of Systems and Software
Automated diagnosis of feature model configurations
Journal of Systems and Software
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applications is complex and error-prone, involving multiple participants/roles and numerous configuration changes across multiple files, application server settings, and database decisions. This paper describes an approach to automated enterprise application configuration that uses a feature model, executes a series of probes to verify configuration properties, formalizes feature selection as a constraint satisfaction problem, and applies constraint logic programming techniques to derive a correct application configuration. To validate the approach, we developed a configuration engine, called Fresh, for enterprise Java appli- cations and conducted experiments to measure how effectively Fresh can configure the canonical Java Pet Store application. Our results show that Fresh reduces the number of lines of hand written XML code by up to 92% and the total number of configuration steps by up to 72%.