  Software product line (SPL) engineers put a lot of effort to ensure that,
through the setting of a large number of possible configuration options,
products are acceptable and well-tailored to customers' needs. Unfortunately,
options and their mutual interactions create a huge configuration space which
is intractable to exhaustively explore. Instead of testing all products,
machine learning techniques are increasingly employed to approximate the set of
acceptable products out of a small training sample of configurations. Machine
learning (ML) techniques can refine a software product line through learned
constraints and a priori prevent non-acceptable products to be derived. In this
paper, we use adversarial ML techniques to generate adversarial configurations
fooling ML classifiers and pinpoint incorrect classifications of products
(videos) derived from an industrial video generator. Our attacks yield (up to)
a 100% misclassification rate and a drop in accuracy of 5%. We discuss the
implications these results have on SPL quality assurance.
