  Highly-configurable software systems can have thousands of interdependent
configuration options across different subsystems. In the resulting
configuration space, discovering a valid product configuration for some
selected options can be complex and error prone. The configuration space can be
organized using a feature model, fragmented into smaller interdependent feature
models reflecting the configuration options of each subsystem.
  We propose a method for lazy product discovery in large fragmented feature
models with interdependent features. We formalize the method and prove its
soundness and completeness. The evaluation explores an industrial-size
configuration space. The results show that lazy product discovery has
significant performance benefits compared to standard product discovery, which
in contrast to our method requires all fragments to be composed to analyze the
feature model. Furthermore, the method succeeds when more efficient,
heuristics-based engines fail to find a valid configuration.
