Improved sparrow search algorithm for test redundancy reduction

The Sparrow Search Algorithm (SSA), a kind of innovative swarm intelligence algorithm, has been used in a variety of domains due to its special qualities, such as its robust global search capabilities, its limited number of adjustable parameters, and its clear structure. However, the SSA still has some weaknesses that prevent its further development. These weaknesses include low population diversity, limited local searchability, and a tendency to easily slip into local optima. Software testing is critical to fulfilling the user requirement. the software is tested using test cases, and it is impossible to run all test cases in a regression test suite because the size of the test suite increases as the software changes over time, and this would take a lot of time and effort. There are numerous approaches to minimize test suites, but none of them can create a test suite with the ideal number of tests because the problem is NP-complete. In this context, in this paper, we proposed an improved sparrow search algorithm, and we use the test redundancy reduction problem as a case study. Therefore, our algorithm has shown promising and superior results compared to standard SSA.