A multiobjective approach to search-based test data generation

There has been a considerable body of work on search–based test data generation for branch coverage. However, hitherto, there has been no work on multi–objective branch coverage. In many scenarios a single–objective formulation is unrealistic; testers will want to find test sets that meet several objectives simultaneously in order to maximize the value obtained from the inherently expensive process of running the test cases and examining the output they produce. This paper introduces multi–objective branch coverage. The paper presents results from a case study of the twin objectives of branch coverage and dynamic memory consumption for both real and synthetic programs. Several multi– objective evolutionary algorithms are applied. The results show that multi–objective evolutionary algorithms are suitable for this problem, and illustrates the way in which a Pareto optimal search can yield insights into the trade–offs between the two simultaneous objectives