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Comprehending Feature Models Expressed in CVL

Abstract

Feature modeling is a common way to present and manage variability of software and systems. As a prerequisite for effective variability management is comprehensible representation, the main aim of this paper is to investigate difficulties in understanding feature models. In particular, we focus on the comprehensibility of feature models as expressed in Common Variability Language (CVL), which was recommended for adoption as a standard by the Architectural Board of the Object Management Group. Using an experimental approach with participants familiar and unfamiliar with feature modeling, we analyzed comprehensibility in terms of comprehension score, time spent to complete tasks, and perceived difficulty of different feature modeling constructs. The results showed that familiarity with feature modeling did not influence the comprehension of mandatory, optional, and alternative features, although unfamiliar modelers perceived these elements more difficult than familiar modelers. OR relations were perceived as difficult regardless of the familiarity level, while constraints were significantly better understood by familiar modelers. The time spent to complete tasks was higher for familiar modelers.

Category

Academic article

Client

  • Research Council of Norway (RCN) / 221346

Language

English

Author(s)

  • Iris Reinhartz-Berger
  • Kathrin Figl
  • Øystein Haugen

Affiliation

  • University of Haifa
  • Austria
  • University of Oslo
  • SINTEF Digital / Software and Service Innovation

Year

2014

Published in

Lecture Notes in Computer Science (LNCS)

ISSN

0302-9743

Publisher

Springer

Volume

8767

Page(s)

501 - 517

View this publication at Cristin