| Abstrakt: |
With the continuous development of Internet technology, the functional requirements of all kinds of business software are increasing, and their complexity is gradually increasing. The reliability and security of software have attracted more and more attention. Software testing is the key technology for software quality assurance. Due to the frequent changes in requirements and rapid version iteration of modern business software products, manually writing test cases for them will consume a lot of manpower costs, especially in agile development processes where regression testing and other tasks require a large number of repeated use cases. A test case automatic generation model based on genetic algorithm and LightGBM model is proposed using machine learning technology. The innovative contribution lies in: 1 Abstracting the test steps into a directed graph model, the test case data were simplified; 2 The genetic algorithm was used to solve reachable paths in directed graphs, replacing manually generated test paths; 3 Using the LightGBM model to accelerate the convergence speed of the genetic algorithm, the effectiveness of the proposed method was experimentally verified, meeting the testing coverage criteria. This model can reduce the workload of testers, accelerate testing speed, and is of great significance for improving project quality and accelerating project progress. [ABSTRACT FROM AUTHOR] |
