A well-designed fitness function is essential to the effectiveness and efficiency of evolutionary testing. Fitness function design has been researched extensively. For fitness calculation, so far the switchcase construct has been regarded as a nested if-else structure with respect to the control flow. Given a target embraced in a case branch, test data taking different case branches receive different approximation levels. Since the approximation levels received by test data do not evaluate their suitability accurately, the guidance provided by the existing approach to evolutionary search is misleading or lost. Despite the switch-case construct’s wide use in industrial applications, no previous work has addressed this problem. In this paper,...
Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world appl...
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve diff...
Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simula...
Abstract. Evolutionary structural testing is a technique that uses specific approaches based on guid...
International audienceIn Genetic Programming (GP), the fitness of individuals is normally computed b...
In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitne...
In evolutionary testing of an object-oriented program, the search objective is to find a sequence of...
In search based test case generation, most of the research works focus on the single-objective formu...
Previous research using genetic algorithms to automate the generation of data for path testing has u...
Genetic programming systems typically use a fixed training population to optimize programs according...
Evolutionary testing is an approach to automating test data generation that uses an evolutionary alg...
Test data generation is one of the main tasks of software testing. The goal of test data generation ...
Fitness functions derived from certain types of white-box test goals can be inadequate for evolution...
Abstract Background The canonical code, although prevailing in complex genomes, is not universal. It...
Abstract. Fitness functions derived for certain white-box test goals can cause problems for Evolutio...
Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world appl...
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve diff...
Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simula...
Abstract. Evolutionary structural testing is a technique that uses specific approaches based on guid...
International audienceIn Genetic Programming (GP), the fitness of individuals is normally computed b...
In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitne...
In evolutionary testing of an object-oriented program, the search objective is to find a sequence of...
In search based test case generation, most of the research works focus on the single-objective formu...
Previous research using genetic algorithms to automate the generation of data for path testing has u...
Genetic programming systems typically use a fixed training population to optimize programs according...
Evolutionary testing is an approach to automating test data generation that uses an evolutionary alg...
Test data generation is one of the main tasks of software testing. The goal of test data generation ...
Fitness functions derived from certain types of white-box test goals can be inadequate for evolution...
Abstract Background The canonical code, although prevailing in complex genomes, is not universal. It...
Abstract. Fitness functions derived for certain white-box test goals can cause problems for Evolutio...
Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world appl...
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve diff...
Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simula...