Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74243
Title: Constructing feature model by identifying variability-aware modules
Authors: Tang, Y 
Leung, H 
Issue Date: 2017
Source: IEEE International Conference on Program Comprehension, 2017, 7961523, p. 263-274
Abstract: Modeling variability, known as building feature models, should be an essential step in the whole process of product line development, maintenance and testing. The work on feature model recovery serves as a foundation and further contributes to product line development and variability-aware analysis. Different from the architecture recovery process even though they somewhat share the same process, the variability is not considered in all architecture recovery techniques. In this paper, we proposed a feature model recovery technique VMS, which gives a variability-aware analysis on the program and further constructs modules for feature model mining. With our work, we bring the variability information into architecture and build the feature model directly from the source base. Our experimental results suggest that our approach performs competitively and outperforms six other representative approaches for architecture recovery.
Keywords: Configuration
Feature model recovery
Feature modules
Product line
Variability-aware modularity
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 9781538605356
DOI: 10.1109/ICPC.2017.21
Appears in Collections:Conference Paper

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