Using feature model knowledge to speed up the generation of covering arrays

  • Authors:
  • Evelyn Nicole Haslinger;Roberto E. Lopez-Herrejon;Alexander Egyed

  • Affiliations:
  • Johannes Kepler University Linz, Austria;Johannes Kepler University Linz, Austria;Johannes Kepler University Linz, Austria

  • Venue:
  • Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems
  • Year:
  • 2013

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Abstract

Combinatorial Interaction Testing has shown great potential for effectively testing Software Product Lines (SPLs). An important part of this type of testing is determining a subset of SPL products in which interaction errors are more likely to occur. Such sets of products are obtained by computing a so called t-wise Covering Array (tCA), whose computation is known to be NP-complete. Recently, the ICPL algorithm has been proposed to compute these covering arrays. In this research-in-progress paper, we propose a set of rules that exploit basic feature model knowledge to reduce the number of elements (i.e. t-sets) required by ICPL without weakening the strength of the generated arrays. We carried out a comparison of runtime performance that shows a significant reduction of the needed execution time for the majority of our SPL case studies.