Title: A Hybrid Approach of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) Algorithm for Test Case Optimization


Sultan Singh Saini

Department of Computer Science, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur (INDIA),

Vipin Jain

Department of Computer Science, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur (INDIA)


Software testing is a phase of the software development lifecycle model. It is a special programming framework that tries to find errors. The tester compares the expected or generated results with the original results or previous results. The test method manages the testers and exposes them to the reference points that the testers need to complete. The existing frame work uses Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Hybrid model for test case prioritization, although the basic destination is statement coverage and fault coverage, because it used to be ABC Algorithm, so it is the convergence rate is moderate. Likewise, ABC algorithm based on greedy method, some great solution cannot be easily ignored because greedy method chosen now seems the best arrangement. Because of this, the proposed method, rather than adding the PSO algorithm and the ABC algorithm, merges the PSO algorithm with the ACO as ensured by the algorithm convergence rate will be described. In addition, regression testing in the case of our application retests all regression testing technology, which means that we will no doubt perform all the test cases in each iteration. This result in extra effort and swallows up a lot of time and assets. In any case, due to the retesting of time and
asset requirements, all methods cannot be implemented in any way. To overcome this problem, the proposed method uses a hybrid approach of regression testing that incorporates regression test priorities and regression test choices. This paper shows a hybrid approach that allows the selection and prioritization at different levels. The goal is to use the prioritized grouping after selection. Subsequently, the prioritization and selection arrangement reduces the number of test cases in which the code is executed in this manner, thereby reducing running and testing time by utilizing minimal resources.