Paper Info
Title
Integration of feature models: A systematic mapping study.
Abstract
Abstract Context The integration of feature models has been widely investigated in the last decades, given its pivotal role for supporting the evolution of software product lines. Unfortunately, academia and industry have overlooked the production of a thematic analysis of the current literature. Hence, a thorough understanding of the state-of-the-art works remains still limited. Objective This study seeks to create a panoramic view of the current literature to pinpoint gaps and supply insights of this research field. Method A systematic mapping study was performed based on well-established empirical guidelines for answering six research questions. In total, 47 primary studies were selected by applying a filtering process from a sample of 2874 studies. Results The main results obtained are: (1) most studies use a generic notation (68.09%, 32/47) for representing feature models; (2) only one study (2%, 1/47) compares feature models based on their syntactic and semantics; (3) there is no preponderant use of a particular integration technique in the selected studies; (4) most studies (70%, 33/47) provide a product-based strategy to evaluate the integrated feature models; (5) majority (70%, 33/47) automates the integration process; and (6) most studies (90%, 42/47) propose techniques, rather than focusing on producing practical knowledge derived from empirical studies. Conclusion The results were encouraging and suggest that integration of feature models is still an evolving research area. This study provides insightful information for the definition of a more ambitious research agenda. Lastly, empirical studies exploring the required effort to apply the current integration techniques in real-world settings are highly recommended in future work.
Year
DOI
Venue
2019
10.1016/j.infsof.2018.08.016
Information and Software Technology
Keywords
Field
DocType
Feature models,Model integration,Systematic mapping study
Data science,Thematic analysis,Data mining,Notation,Systematic mapping,Computer science,Filter (signal processing),Software,Syntax,Empirical research,Semantics
Journal
Volume
ISSN
Citations 
105
0950-5849
0
PageRank 
References 
Authors
0.34
45
4
Name
Order
Citations
PageRank
Vinícius Bischoff111.02
Kleinner Farias24315.61
Lucian José Gonçales323.07
Jorge Luis Victória Barbosa47329.37