Anomaly detection and explanation in context-aware software product lines

A software product line (SPL) uses a variability model, such as a feature model (FM), to describe the configuration options for a set of closely related software systems. Context-aware SPLs also consider possible environment conditions for their configuration options. Errors in modeling the FM and its context may lead to anomalies, such as dead features or a void feature model, which reduce if not negate the usefulness of the SPL. Detecting these anomalies is usually done by using Boolean satisfiability (SAT) that however are not expressive enough to detect anomalies when context is considered. In this paper, we describe HyVarRec: A tool that relies on Satisfiability Modulo Theory (SMT) to detect and explain anomalies for context-aware SPLs.