A Multi-Objective Approach for Test Suite Reduction During Testing of Web Applications: A Search-Based Approach

Munish Khanna, Naresh Chauhan, Dilip Kumar Sharma and Law Kumar Singh
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Munish Khanna: Hindustan College of Science and Technology, Mathura, India
Naresh Chauhan: YMCA University of Science and Technology, Faridabad, India
Dilip Kumar Sharma: GLA University, Mathura, India
Law Kumar Singh: Hindustan College of Science and Technology, Mathura, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 3, 81-122

Abstract: During the development and maintenance phases of evolving software, new test cases would be needed for the verification of the accuracy of the modifications as well as for new functionalities leading to an increase in the size of the test suite. Various related objectives are to be kept in mind while reducing the original test suite by removing redundancy and generating a practical representative set of the unique test cases, some of which may need to be maximized and the remaining ones minimized. This paper presents a multi-objective approach for the test suite reduction problem in which one objective is to be minimized and the remaining two maximized. In this study, experiments were performed on diverse versions of four web applications. Various state-of-the-art algorithms and their updated versions were compared with non-dominated sorting genetic algorithm-II (NSGA-II) for performance evaluation. Based on experimental findings, it was concluded that NSGA-II outperforms all other algorithms; moreover, the algorithm attempts to satisfy all the objectives without compromising coverage.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:12:y:2021:i:3:p:81-122

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