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