Product selection based on upper confidence bound MOEA/D-DRA for testing software product lines

Abstract

The selection of products for testing Software Product Lines (SPLs) is an optimization problem. The goal is to select a possible minimum set of products that satisfies testing criteria, such as, pairwise and mutation testing. Multi-objective Evolutionary Algorithms (MOEAs) have been successfully used to solve this problem and other ones related to software development. However, the use of MOEAs demands setting a number of control parameters and selection of genetic operators, to which the algorithm performance is often very sensitive. Adaptive Operator Selection (AOS) methods, such as Upper Confidence Bound (UCB) based ones can help in this task. UCB methods used with Multi-objective Evolutionary Algorithm Based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) have presented promising results, but they are underexplored in the Search Based Software Engineering (SBSE) field. To contribute to this research area and to solve efficiently the product selection problem, this paper investigates the use of different AOS UCB-based methods with MOEA/D-DRA. The idea is to reduce effort spent by the tester. Some parameters and evolutionary operators can be automatically set. The approach is empirical evaluated using four instances and three UCB methods. The UCB methods present similar results and outperform the canonical version of MOEA/D-DRA.