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Test Data Generation for Branch Coverage in Software Structural Testing Based on TLBO
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Author(s): Updesh Kumar Jaiswal (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Amarjeet Prajapati (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 13
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch016
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Abstract
Test data generation is forever a core task in automated software testing (AST). Recently, some meta-heuristic search-based techniques have been examined as a very effective approach to facilitate test data generation in the structural testing of software. Although the existing methods are satisfactory, there are still opportunities for further improvement and enhancement. To solve, automate, and assist the test data generation process in software structural testing, a teaching learning based optimization (TLBO) algorithm is adapted in this chapter. In this proposed method, the branch coverage convention is taken as a fitness function to optimize the solutions. For validation of the proposed method, seven familiar and benchmark software programs from the literature are utilized. The experimental results show that the proposed method, mostly, surpasses simulated annealing, genetic algorithm, harmony search, particle swarm optimization, ant colony optimization, and artificial bee colony.
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