A new approach for test case generation by discrete particle swarm optimization algorithm

The increasing complexities in softwares, have reduced the efficiency of the common methods of software testing and urged the necessity to use new and optimal methods to produce test case (TC) to cover high percentage of target plan to find existing errors. Thus, today, the production of TC is considered to be an important aim in software testing methods. Particle Swarm Optimization (PSO) is an intelligent technique based on the collective movement of the particles inspired by social behavior of the flocks of birds and schools of fish. After full introduction of this algorithm and the reasons behind usage PSO, the present study will propose a method for automatic production of the TC, such that the highest code covering is done by the minimum TC. For better analysis of the results, a plan was investigated as a case study with the proposed method. As the evolutionary structures such as genetic algorithm (GA) with high percent of code covering are being used for a long time, thus to prove the optimization of the proposed method, the results are compared with the GA.