  Feature Models are a mechanism to organize the configuration space and
facilitate the construction of software variants by describing configuration
options using features, i.e., a name representing a functionality. The
development of Feature Models is an error prone activity and detecting their
anomalies is a challenging and important task needed to promote their usage.
  Recently, Feature Models have been extended with context to capture the
correlation of configuration options with contextual influences and user
customizations. Unfortunately, this extension makes the task of detecting
anomalies harder. In this paper, we formalize the anomaly analysis in
Context-aware Feature Models and we show how Quantified Boolean Formula (QBF)
solvers can be used to detect anomalies without relying on iterative calls to a
SAT solver. By extending the reconfigurator engine HyVarRec, we present
findings evidencing that QBF solvers can outperform the common techniques for
anomaly analysis.
