A Hybrid Approach for Automated GUI Testing Using Quasi-Oppositional Genetic Sparrow Search Algorithm

Testing of GUI is crucial for assessing software reliability, usability, and functionality; however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web.