Comparing algorithms for efficient feature model slicing
Feature models are a well known concept to represent variability in software product lines by defining features and their dependencies. During feature model evolution, for information hiding, and for feature model analyses, it is often necessary to remove certain features from a model. As the crude deletion of features can have undesirable effects on their dependencies, dependency preserving algorithms, known as feature model slicing, have been proposed. However, current algorithms do not perform well when removing a high number of features from large feature models. Therefore, we propose an efficient algorithm for feature model slicing based on logical resolution and the minimization of logical formulas. We empirically evaluate the scalability of our algorithm on a number of feature models and find that our algorithm generally outperforms existing algorithms.
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