Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

Software testing aims to search a set of test data in the entire search space to satisfy a certain standard of coverage. Therefore, finding an effective approach for automatic test data generation is a key issue of software testing. This paper proposes a new approach of reduced adaptive particle swarm optimization for generating the test data automatically. First, the approach reduces the particle swarm evolution equations and gets an evolution equation without velocity. Then, the approach makes an adaptive adjustment scheme based on inertia weight for the reduced evolution equation, which is different from the methods that directly act on the particle velocity in the past. The approach directly impacts on the particle position, namely actual problem solution. Next, according to the particle fitness and the particle aggregation degree, the population will be divided into three parts and inertia weight of each part will be designed accordingly. This can balance the search capabilities of algorithm between global and local. Finally, the approach is applied to automatic test data generation. The experiments results show that our approach can enhance convergence speed of algorithm and solve the problems that particle swarm algorithm easily falls into the local optimal solution and has low search accuracy. The experiments results also turn out that our approach can improve the efficiency of generating test data automatically.