Optimizing the Product Derivation Process

Feature modeling is widely used in software product-line engineering to capture the commonalities and variabilities within an application domain. As feature models evolve, they can become very complex with respect to the number of features and the dependencies among them, which can cause the product derivation based on feature selection to become quite time consuming and error prone. We address this problem by presenting techniques to find good feature selection sequences that are based on the number of products that contain a particular feature and the impact of a selected feature on the selection of other features. Specifically, we identify a feature selection strategy, which brings up highly selective features early for selection. By prioritizing feature selection based on the selectivity of features our technique makes the feature selection process more efficient. Moreover, our approach helps with the problem of unexpected side effects of feature selection in later stages of the selection process, which is commonly considered a difficult problem. We have run our algorithm on the e-Shop and Berkeley DB feature models and also on some automatically generated feature models. The evaluation results demonstrate that our techniques can shorten the product derivation processes significantly.