Modeling how contextual factors relate to a software system’s configuration space is usually a manual, error-prone task that depends highly on expert knowledge. Machine-learning techniques can automatically predict the acceptable software configurations for a given context. Such an approach executes and observes a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software’s features. This lets developers and product managers automatically extract the rules that specialize highly configurable systems for specific contexts.