Authors:
Fathiya Mohammed
1
;
Mike Mannion
2
;
Hermann Kaindl
3
and
James Paterson
2
Affiliations:
1
School of Computing, Engineering & Physical Sciences, University of West of Scotland, Paisley, U.K.,
;
2
Department of Computing, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow, G4 0BA, U.K.,
;
3
Institute of Computer Technology, TU Wien, Austria,
Abstract:
A software product line is a set of products that share a set of software features and assets, which satisfy the specific needs of one or more target markets. One common artefact of software product line engineering is a feature model, usually represented as a directed acyclic graph, which shows the product line as a set of structural feature relationships. We argue that there are benefits to considering a feature model as a directed graph and an undirected graph, respectively. One element of managing the impact of a change to these models, as they increase in complexity, is to evaluate the relative importance of the features. This paper explores the application of centrality metrics from social network analysis for the identification of the relative importance of features in feature models. The metrics considered are degree centrality, closeness centrality, eccentricity centrality, eigenvector centrality and between-ness centrality. To illustrate, a product feature model is construc
ted from a real-world GSMA AI-mobile phone product line requirements specification.
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