NDLI: Optimizing the Product Derivation Process

Content Provider	IEEE Xplore Digital Library
Author	Sheng Chen Erwig, M.
Copyright Year	2011
Abstract	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.
Starting Page	35
Ending Page	44
File Size	319464
Page Count	10
File Format	PDF
ISBN	9781457710292
DOI	10.1109/SPLC.2011.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature Selection Algebra Decision Sequence Computational modeling Decision making Transforms Feature Model Cameras Educational institutions Software
Content Type	Text
Resource Type	Article

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