An inductive learning perspective on automated generation of feature models from given product specifications

For explicit representation of commonality and variability of a product line, a feature model is mostly used. An open question is how a feature model can be inductively learned in an automated way from a limited number of given product specifications in terms of features. We propose to address this problem through machine learning, more precisely inductive generalization from examples. However, no counter-examples are assumed to exist. Basically, a feature model needs to be complete with respect to all the given example specifications. First results indicate the feasibility of this approach, even for generating hierarchies, but many open challenges remain