An Adaptive PSO-based Approach for Data Flow Coverage of a Program

Abstract

Software testing is an important and expensive activity of the software development life cycle. Software testing includes test data generation and application of a test adequacy criterion. There has been an extensive application of meta-heuristic search algorithms to generate software test data for branch coverage and path coverage test adequacy criteria. However, test data generation for data-flow coverage test adequacy criterion remains a challenging task. Genetic algorithm and its variants have been the choice of researchers for automated test data generation. In recent years, other highly-adaptive swarm intelligence techniques such as Particle Swarm Optimization has also been applied for automated test data generation. In this paper, Particle Swarm Optimization algorithm with adaptive inertia weight strategy is used to generate test data for data-flow dependencies of a program. The proposed approach is evaluated on a set of benchmark programs, the measures considered are mean number of generations and mean percentage coverage achieved. The performance of the proposed approach is compared with that of Genetic Algorithm and random search. Over several experiments, it is shown that the proposed approach performed significantly better than random search and Genetic Algorithm in data-flow test data generation and optimization with an increasing performance gap for more complex subject programs.