Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.