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Software and Systems Modeling

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26-05-2015 | Theme Section Paper

Empirical assessment of two approaches for specifying software product line use case scenarios

Authors: Rodrigo Bonifácio, Paulo Borba, Cristiano Ferraz, Paola Accioly

Published in: Software and Systems Modeling | Issue 1/2017

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Abstract

Modularity benefits, including the independent maintenance and comprehension of individual modules, have been widely advocated. However, empirical assessments to investigate those benefits have mostly focused on source code, and thus, the relevance of modularity to earlier artifacts is still not so clear (such as requirements and design models). In this paper, we use a multimethod technique, including designed experiments, to empirically evaluate the benefits of modularity in the context of two approaches for specifying product line use case scenarios: PLUSS and MSVCM. The first uses an annotative approach for specifying variability, whereas the second relies on aspect-oriented constructs for separating common and variant scenario specifications. After evaluating these approaches through the specifications of several systems, we find out that MSVCM reduces feature scattering and improves scenario cohesion. These results suggest that evolving a product line specification using MSVCM requires only localized changes. On the other hand, the results of six experiments reveal that MSVCM requires more time to derive the product line specifications and, contrasting with the modularity results, reduces the time to evolve a product line specification only when the subjects have been well trained and are used to the task of evolving product line specifications.

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This panel occurred at the 10th Software Product Line Conference.

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Title: Empirical assessment of two approaches for specifying software product line use case scenarios
Authors: Rodrigo Bonifácio
Paulo Borba
Cristiano Ferraz
Paola Accioly
Publication date: 26-05-2015
Publisher: Springer Berlin Heidelberg
Published in: Software and Systems Modeling / Issue 1/2017
Print ISSN: 1619-1366
Electronic ISSN: 1619-1374
DOI: https://doi.org/10.1007/s10270-015-0471-3

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