  We investigate the suitability of statistical model checking techniques for
analysing quantitative properties of software product line models with
probabilistic aspects. For this purpose, we enrich the feature-oriented
language FLan with action rates, which specify the likelihood of exhibiting
particular behaviour or of installing features at a specific moment or in a
specific order. The enriched language (called PFLan) allows us to specify
models of software product lines with probabilistic configurations and
behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov
chains. The Maude implementation of PFLan is combined with the distributed
statistical model checker MultiVeStA to perform quantitative analyses of a
simple product line case study. The presented analyses include the likelihood
of certain behaviour of interest (e.g. product malfunctioning) and the expected
average cost of products.
