SFLA-based heuristic method to generate software structural test data

Abstract

Software testing is one of the significant stages in software development life cycle which is a costly and time-consuming task. Automatic tests data generation is one of the traditional techniques to reduce the cost and time spent in software testing. Different evolutionary algorithms have been proposed to generate test data which cover target paths in a software program. In this paper, shuffled frog leaping algorithm (SFLA) is proposed to generate structural test data. The proposed SFLA algorithm is characterized by high convergence speed and simple implementation. In the proposed SFLA, branch coverage is used as the fitness function to generate effective test data. For comparing the performance of the proposed SFLA with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC), seven benchmark programs were used. The results indicated that the proposed SFLA has an average of 99.99% for branch coverage, average 99.97% for success rate, and 2.03 for the average number of generation for covering all branches.