Genetic programming for multi-objective test data generation in search based software testing

Abstract

Software testing is an indispensable part in software development to ensure the quality of products. Multi-objective test data generation is a sub-area of search-based software testing, which focuses on automatically generating test data to form high quality test suites. Due to the limited data representation and the lack of specific multi-objective optimization methods, existing approaches have drawbacks in dealing with real-world programs. This paper presents a new approach to multi-objective test data generation problems using genetic programming (GP), while two genetic algorithm (GA) based approaches are also implemented for comparison purposes. Furthermore, three multi-objective optimization frameworks are used and compared to examine the performance of the GP-based methods. Experiments have been conducted on two types of test data generation problems: integer and double. Each consists of 160 benchmark programs with different degrees of nesting. The results suggest that the new GP approaches perform much better than the two GA-based approaches, and a random search baseline algorithm.