Improving CASA Runtime Performance by Exploiting Basic Feature Model Analysis
This work addresses runtime performance issues for software engineers testing families of systems in Software Product Line Engineering, representing an incremental improvement.
The authors tackled the NP-complete problem of computing t-wise Covering Arrays for Software Product Lines by applying reduction rules based on basic feature model analysis to the CASA simulated annealing algorithm, resulting in an average speedup of 61.8% in median execution time while maintaining coverage.
In Software Product Line Engineering (SPLE) families of systems are designed, rather than developing the individual systems independently. Combinatorial Interaction Testing has proven to be effective for testing in the context of SPLE, where a representative subset of products is chosen for testing in place of the complete family. Such a subset of products can be determined by computing a so called t-wise Covering Array (tCA), whose computation is NP-complete. Recently, reduction rules that exploit basic feature model analysis have been proposed that reduce the number of elements that need to be considered during the computation of tCAs for Software Product Lines (SPLs). We applied these rules to CASA, a simulated annealing algorithm for tCA generation for SPLs. We evaluated the adapted version of CASA using 133 publicly available feature models and could record on average a speedup of $61.8\%$ of median execution time, while at the same time preserving the coverage of the generated array.