@Conference{DBLP:conf/cec/Lopez-HerrejonFCEA14,
  author    = {Roberto E. Lopez{-}Herrejon and Javier Ferrer and Francisco Chicano and Alexander Egyed and Enrique Alba},
  title     = {Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines},
  booktitle = {Proceedings of the Congress on Evolutionary Computation (WCCI 2014), Beijing, China},
  year      = {2014},
  pages     = {387-396},
  abstract  = {Software Product Lines (SPLs) are families of related software products,
	each with its own set of feature combinations. Their commonly large
	number of products poses a unique set of challenges for software
	testing as it might not be technologically or economically feasible
	to test of all them individually. SPL pairwise testing aims at selecting
	a set of products to test such that all possible combinations of
	two features are covered by at least one selected product. Most approaches
	for SPL pairwise testing have focused on achieving full coverage
	of all pairwise feature combinations with the minimum number of products
	to test. Though useful in many contexts, this singleobjective perspective
	does not reflect the prevailing scenario where software engineers
	do face trade-offs between the objectives of maximizing the coverage
	or minimizing the number of products to test. In contrast and to
	address this need, our work is the first to propose a classical multi-objective
	formalisation where both objectives are equally important. In this
	paper, we study the application to SPL pairwise testing of four classical
	multiobjective evolutionary algorithms. We developed three seeding
	strategies - techniques that leverage problem domain knowledge -
	and measured their performance impact on a large and diverse corpus
	of case studies using two well-known multiobjective quality measures.
	Our study identifies the performance differences among the algorithms
	and corroborates that the more domain knowledge leveraged the better
	the search results. Our findings enable software engineers to select
	not just one solution (as in the case of single-objective techniques)
	but instead to select from an array of test suite possibilities the
	one that best matches the economical and technological constraints
	of their testing context.},
  doi       = {10.1109/CEC.2014.6900473},
  file      = {:Conferences\\WCCI 2014 - Comparative analysis of Classical Multi-Objective Evolutionary Algorithms for Software Product Lines\\Comparative analysis of Classical Multi-Objective Evolutionary Algorithms for Software Pr-preprint.pdf:PDF},
  keywords  = {FWF P25289, FWF M1421},
}