Generating Optimal Test Cases Using Elitist Genetic Algorithm
Year: 2025
Type: article
Abstract: In order to reduce the number of test cases that do not significantly improve the mean of test coverage or where the test cases are unable to isolate mistakes, this research study examines the use and efficacy of the Elitist genetic algorithm. In this work, a genetic algorithm is used to help minimize or optimize the test cases. The algorithm creates the initial population at random, determines the fitness value using coverage metrics, and then uses genetic operations such as selection, cross-over, and mutation to select the offspring in successive generations. Specific genetic modeling processes may differ from standard genetic algorithms depending on the task. This generation process is performed until the fitness values remain unchanged for two successive generations. Convergence or a reduced test case is reached when the data generation remains unchanged for two iterations. The findings of study reveal that, genetic algorithms can greatly reduce the amount of the test cases
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7083289084
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