Genetic Programming for Multi-objective Test Data Generation in Search Based Software Testing

• Published in: AI 2017: Advances in Artificial Intelligence Vol. 10400; pp. 169 - 181
• Main Authors: Huo, Jiatong, Xue, Bing, Shang, Lin, Zhang, Mengjie
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Automatic test data generation; Genetic programming; Multi-objective optimization; Software testing
• ISBN: 9783319630038; 3319630032
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-63004-5_14

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.