Research on Generation Algorithm of Parallel Particle Swarm Algorithm Combination Test Cases

Abstract

At this stage, software testing is the core means to ensure software quality and improve software reliability. At the same time, improving and generating the degree of automation corresponding to test cases is also the key to improving the automation level of software testing. In order to further improve the automation of generating test cases, it is necessary to reasonably optimize the inherent algorithm of the basic particle swarm. The inherent particle swarm algorithm takes more time to generate combined test cases. In response to this situation, a new parallel particle swarm algorithm is proposed to generate a pairwise combination test case method, and the big data platform spark is used as the basis to group all the use cases that need to be covered in the form of pairwise combination processing. The final result is sent to the corresponding cluster node for continued optimization. The one-test-at-a-time strategy is combined with the adaptive particle swarm algorithm to find the optimal solution. After the optimization operation of all nodes is completed, spark can be used to collect all the results produced. At this time, the collected and processed results can be simplified and processed through the use case set. The algorithm proposed in this paper mainly improves the automatic test case generation algorithm, so that the iterative prompting and average running time required for automatic stress generation are better than the traditional algorithm to achieve the purpose of improving the degree of automatic test generation.