Comparative Analysis of Ant Colony Optimization and Particle Swarm Optimization for Test Case Prioritization
A. Mugilan, Tushar Totla, Yash Renwa, R. Charanya, Stalin Subbiah, T. B. Dharmaraj, S. Om Prakash · 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · 2022
To boost the efficiency of testing and save time and money in the construction of the testing program, the test case prioritization technique prioritizes a subset of the full test suite and optimizes the execution order of the test cases. The goal of this paper is to classify and compare the performance of two nature-based test case prioritization techniques: To overcome the problem of needing to perform the whole test suite at some point, resulting in time and expense limits, we used Ant Colony Optimization and Particle Swarm Optimization. We chose a sample test suite and prioritized the test cases for our research. An experimental investigation of the acquired data will be useful in selecting the best prioritizing technique under various environmental constraints. For our analysis, we selected a sample test suite and prioritized the test cases. In various environmental restrictions, an experimental study of the findings collected will be valuable in determining the optimum prioritization strategy. The findings of both algorithms showed high global optimization abilities, with the ant colony strategy outperforming the particle swarm optimization approach.