A Mutation and Multi-objective Test Data Generation Approach for Feature Testing of Software Product Lines

Abstract

Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Feature Model (FM). In this way, the operators and mutation score can be used to evaluate and generate a test set, that is a set of SPL products to be tested. However, the generation of test sets to kill all the mutants with a reduced, possible minimum, number of products is a complex task. To solve such problem, this paper introduces a multiobjective approach that includes a representation to the problem, search operators and two objectives related to the number of test cases and dead mutants. The approach was implemented with three representative multi-objective and evolutionary algorithms: NSGA-II, SPEA2 and IBEA. The conducted evaluation analyses the solutions obtained and compares the algorithms. An advantage of this approach is to offer a set of good solutions to the tester with a reduced number of products and high mutation score values, that is, with high probability of revealing faults described by the mutation testing.