Using feature model knowledge to speed up the generation of covering arrays. (bibtex)
by Evelyn N. 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:
Using feature model knowledge to speed up the generation of covering arrays. (Evelyn N. Haslinger, Roberto E. Lopez-Herrejon, Alexander Egyed), 2013.
Bibtex Entry:
@Workshop{DBLP:conf/vamos/HaslingerLE13,
  author    = {Evelyn N. Haslinger and Roberto E. Lopez-Herrejon and Alexander Egyed},
  booktitle = {7th International Workshop on Variability Modeling of Software-Intensive Systems (VAMOS), Pisa, Italy},
  title     = {Using feature model knowledge to speed up the generation of covering arrays.},
  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      = {:Workshops\\VAMOS 2013 -Using Feature Model Knowledge To Speed Up the Generation of Covering Arrays\\Using Feature Model Knowledge to Speed Up the Generation of Covering Arrays-preprint.pdf:PDF},
  keywords  = {FWF P21321, FWF M1421},
}
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