On Automated Anomaly Detection for Potentially Unbounded Cardinality-based Feature Models

Abstract

In this work, we report about our research results on analysis of cardinality-based fea- ture models with potentially unbounded feature multiplicities, initially published in [We16]. Feature models are frequently used for specifying variability of user-configurable software systems, e.g., software product lines. Numerous approaches have been developed for automating feature model validation concerning constraint consistency and absence of anomalies. As a crucial extension to feature models, cardinality annotations allow for multiple, and even potentially unbounded occur- rences of feature instances within configurations. This is of particular relevance for user-adjustable application resources as prevalent, e.g., in cloud-based systems where not only the type, but also the amount of available resources is explicitly configurable. However, a precise semantic characteriza- tion and tool support for automated and scalable validation of cardinality-based feature models is still an open issue. We present a comprehensive formalization of cardinality-based feature models with potentially unbounded feature multiplicities. We apply a combination of ILP and SMT solvers to automate consistency checking and anomaly detection, including novel anomalies, e.g., interval gaps. Furthermore, we show evaluation results gained from our tool implementation showing appli- cability and scalability of our approach to larger-scale models.