by Roberto E. Lopez-Herrejon, Francisco Chicano, Javier Ferrer, Alexander Egyed, Enrique Alba
Abstract:
Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pair wise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.
Reference:
Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing. (Roberto E. Lopez-Herrejon, Francisco Chicano, Javier Ferrer, Alexander Egyed, Enrique Alba), In Proceedings of the 28th International Conference on Software Maintenance (ICSM 2013), Riva del Garda, Italy, 2013.
Bibtex Entry:
@Conference{DBLP:conf/icsm/Lopez-HerrejonCFEA13,
author = {Roberto E. Lopez{-}Herrejon and Francisco Chicano and Javier Ferrer and Alexander Egyed and Enrique Alba},
title = {Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing.},
booktitle = {Proceedings of the 28th International Conference on Software Maintenance (ICSM 2013), Riva del Garda, Italy},
year = {2013},
pages = {404-407},
abstract = {Software Product Lines (SPLs) are families of related software products,
which usually provide a large number of feature combinations, a fact
that poses a unique set of challenges for software testing. Recently,
many SPL testing approaches have been proposed, among them pair wise
combinatorial techniques that aim at selecting products to test based
on the pairs of feature combinations such products provide. These
approaches regard SPL testing as an optimization problem where either
coverage (maximize) or test suite size (minimize) are considered
as the main optimization objective. Instead, we take a multi-objective
view where the two objectives are equally important. In this exploratory
paper we propose a zero-one mathematical linear program for solving
the multi-objective problem and present an algorithm to compute the
true Pareto front, hence an optimal solution, from the feature model
of a SPL. The evaluation with 118 feature models revealed an interesting
trade-off between reducing the number of constraints in the linear
program and the runtime which opens up several venues for future
research.},
bibsource = {{dblp computer science bibliography, https://dblp.org}},
biburl = {http://dblp.uni-trier.de/rec/bibtex/conf/icsm/Lopez-HerrejonCFEA13},
doi = {10.1109/ICSM.2013.58},
file = {:Conferences\\ICSME 2013 - Multi-Objective Optimal Test Suite Computation for Software Product Line Pairwise Testing\\Multi-Objective Optimal Test Suite Computation for Software Product Line Pairwise Testing-preprint.pdf:PDF},
keywords = {},
}