Abstract

Feature modeling is a technique in domain analysis to describe the variabilities and commonalities among similar programs. The valid combinations of features are specified in a feature model. Automated analysis of feature models reveals errors and supports the configuration process. Feature models evolve over time and there is a need for edits on feature models. We categorize edits into refactorings, specializations, and generalizations, and arbitrary edits to support the modeling process. This paper presents an algorithm to reason about feature model edits and their categories. Two stand-alone feature models can be taken as input, where the set of features is not necessary identical. We evaluate the algorithm with random feature models to show that the algorithm scales even for larger feature models.