Software engineering is a subject of Engineering Research on software development and software testing is the key component of software engineering, which directly affects the development prospects of software engineering. The experimental results demonstrate that the approach outlined here keeps higher testing case generation efficiency, and it shows certain advantages in coverage, evolution generation amount and running time when compared to standard PSO and GA-PSO. An improved alternating variable method is put forward to accelerate local search speed, which can coordinate both global and local search ability thereby improving the overall generation efficiency of testing cases. It adjusts the inertia weight dynamically according to the current iteration and average relative speed, to improve the performance of standard PSO. To overcome the above defects, a self-adaptive PSO based software testing case optimization algorithm is proposed. The particle swarm optimization (PSO) optimized testing case generation algorithm tends to lose population diversity of locally optimal solutions with low accuracy of local search.
Searching based testing case generation technology converts the problem of testing case generation to function optimizations, through a fitness function, which is usually optimized using heuristic search algorithms.
