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

Haslinger, Evelyn Nicole; Lopez-Herrejon, Roberto E.; Egyed, Alexander

doi:10.1145/2430502.2430524

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

by Evelyn Nicole Haslinger, Roberto E. Lopez-Herrejon, Alexander Egyed

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.

Reference:

Evelyn Nicole Haslinger, Roberto E. Lopez-Herrejon, Alexander Egyed, "Using feature model knowledge to speed up the generation of covering arrays.", pp. 16, 2013.

Bibtex Entry:

@Workshop{DBLP:conf/vamos/HaslingerLE13,
  Title                    = {Using feature model knowledge to speed up the generation of covering arrays.},
  Author                   = {Evelyn Nicole Haslinger and Roberto E. Lopez-Herrejon and Alexander Egyed},
  Booktitle                = {7th International Workshop on Variability Modeling of Software-Intensive Systems (VAMOS), Pisa, Italy},
  Year                     = {2013},

  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.},
  Pages                    = {16},

  Doi                      = {10.1145/2430502.2430524},
  File                     = {Using feature model knowledge to speed up the generation of covering arrays.:Workshops\\VAMOS 2013 -Using Feature Model Knowledge To Speed Up the Generation of Covering Arrays\\CameraReadyVamos2013.pdf:PDF},
  Keywords                 = {variability, testing, FWF P21321-N15, FWF M1421-N15}
}