Adaptive Random Test Case Generation Based on Multi-Objective Evolutionary Search

Abstract

Diversity is the key factor for test cases to detect program failures. Adaptive random testing (ART) is one of the effective methods to improve the diversity of test cases. Being an ART algorithm, the evolutionary adaptive random testing (eAR) only increases the distance between test cases to enhance its failure detection ability. This paper presents a new ART algorithm, MoesART, based on multi-objective evolutionary search. In this algorithm, in addition to the dispersion diversity, two other new diversities (or optimization objectives) are designed from the perspectives of the balance and proportionality of test cases. Then, the Pareto optimal solution returned by the NSGA-II framework is used as the next test case. In the experiments, the typical block failure pattern in the cases of two-dimensional and three-dimensional input domains is used to validate the effectiveness of the proposed MoesART algorithm. The experimental results show that MoesART exhibits better failure detection ability than both eAR and the fixed-sized-candidate-set ART (FSCS-ART), especially for the programs with three-dimensional input domain.