Evolutionary algorithms for object-oriented test data generation

Abstract

Identification of effective test data for testing a software application is a difficult task. The presence of a large number of decision nodes in a program makes it difficult to test all modules, and as a result consumes a lot of testers' time. The effort required for testing can be reduced by automatic generation of test data for particular modules. Out of numerous optimization algorithms, evolutionary algorithms can help in this scenario by generating relevant test data. The ability of evolutionary algorithms to obtain effective solutions from a very large search space of candidate solutions can be used for automatic test data generation. This paper explores the automatic generation of test data for object-oriented programs based on the concept of the extended control flow graph by utilizing the binary particle swarm optimization and artificial bee colony optimization algorithms. The proposed approach is applied to a bank ATM case study. The experimental results obtained, when compared with the clonal selection algorithm, reveal that the artificial bee colony optimization algorithm is more efficient for generating effective test data than the binary particle swarm optimization and clonal selection algorithms.