A novel approach to automate test data generation for data flow testing based on hybrid adaptive PSO-GA algorithm

Abstract

The most important and crucial activity to develop good quality software is software testing. The most important activity in software testing is to find optimal test suite in input domain to satisfy a certain test adequacy criteria. So to develop an efficient approach to generate test data is a prime issue in software testing. This paper proposes a novel approach to generate test data automatically for data flow testing based on hybrid adaptive PSO-GA algorithm. The hybrid APSO-GA is developed to conquer the weakness of GA and PSO algorithms, especially in data flow testing. A new fitness function is also designed on the basis of the concept of dominance relations, branch weight and branch distance to guide the search direction more efficiently. The efficiency of proposed approach is then tested on ten benchmark programs and four real world programs. The proposed approach is then compared with GA, PSO, ACO, DE and hybrid GA-PSO on the basis of two performance parameters, average number of generations and average coverage achieved. The results show that hybrid adaptive PSO-GA gives better results as compared to other algorithms that are used in the field of test data generation.