This paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing. Experimental results demonstrate that our test data generator can generate suitable test data with higher path coverage than the previous one. | VNU Journal of Science: Comp. Science & Com. Eng., Vol. 33, No. 2 (2017) 28-38 Generating Test Data for Software Structural Testing using Particle Swarm Optimization Dinh Ngoc Thi* VNU University of Engineering and Technology, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam Abstract Search-based test data generation is a very popular domain in the field of automatic test data generation. However, existing search-based test data generators suffer fromsome problems. By combining static program analysis and search-based testing, our proposed approach overcomesone of these problems. Considering the automatic ability and the path coverage as the test adequacycriterion, this paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing. Experimental results demonstrate that our test data generator can generate suitable test data with higher path coverage than the previous one. Received 26 Jun 2017; Revised 28 Nov 2017; Accepted 20 Dec 2017 Keywords:Automatic test data generation, search-based software testing, Particle Swarm Optimization. 1. Introduction* as an efficient and necessary method in order to reduce those efforts and costs. Automated structural test data generation is becoming the research topic attracting much interest in automated software testingbecause it enhances the efficiency while reducing considerably costs of software testing. In our paper, we will focus on path coverage test data generation, considering that almost all structural test data generation problems can be transformed to the path coverage test datageneration one. Moreover, Kernighan and Plauger [3] also pointed out that path coverage test data generation can find out more than 65 percent of bugs in the given program under test (PUT). Although path coverage test data generation is the major unsolved problem [20], various approaches have been proposed by researchers. These approaches can be .
