Abstract
Software testing identifies potential errors and defects in software. A crucial component of software testing is integration testing, and the generation of class integration test orders (CITOs) is a critical topic in integration testing. The research shows that search-based algorithms can solve this problem effectively. As a novel search-based algorithm, the sparrow search algorithm (SSA) is good at finding the optimal to optimization problems, but it has drawbacks like weak population variety later on and the tendency to easily fall into the local optimum. To overcome its shortcomings, a modified sparrow search algorithm (MSSA) is developed and applied to the CITO generation issue. The algorithm is initialized with a good point set strategy, which distributes the sparrows evenly in the solution space. Then, the discoverer learning strategy of Brownian motion is introduced and the Levy flight is utilized to renew the positions of the followers, which balances the global search and local search of the algorithm. Finally, the optimal solution is subjected to random wandering to increase the probability of the algorithm jumping out of the local optimum. Using the overall stubbing complexity as a fitness function to evaluate different class test sequences, experiments are conducted on open-source Java systems, and the experimental results demonstrate that the MSSA generates test orders with lower stubbing cost in a shorter time than other novel intelligent algorithms. The superiority of the proposed algorithm is verified by five evaluation indexes: the overall stubbing complexity, attribute complexity, method complexity, convergence speed, and running time. The MSSA has shown significant advantages over the BSSA in all aspects. Among the nine systems, the total overall stubbing complexity of the MSSA is 13.776% lower than that of the BSSA. Total time is reduced by 23.814 s.