Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Shweta Singhal; Nishtha Jatana; Geetika Dhand; Shaily Malik; Kavita Sheoran

doi:10.17485/IJST/v16i14.2109

Article

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i14.2109

Year: 2023, Volume: 16, Issue: 14, Pages: 1038-1044

Original Article

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Shweta Singhal¹, Nishtha Jatana^2*, Geetika Dhand³, Shaily Malik², Kavita Sheoran³

¹Assistant Professor (visiting), Indira Gandhi Delhi Technological University, New Delhi, India
²Assistant Professor, Maharaja Surajmal Institute of Technology, New Delhi, India
³Associate Professor, Maharaja Surajmal Institute of Technology, New Delhi, India

*Corresponding Author
Email: [email protected]

Received Date:04 January 2023, Accepted Date:12 March 2023, Published Date:06 April 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Software researchers have been taking advantage of various evolutionary optimization approaches by digitizing them. Test case selection and prioritization based on fault coverage criteria within a time-constrained environment is important in regression testing problem. Methods: This work empirically evaluates different approaches that includes evolutionary approaches (Ant Colony Optimization, Bee Colony Optimization, a combination of Genetic Algorithms and Bee Colony optimization), and a Greedy approach. These tetrad techniques have been successfully applied to regression testing. Also, tools have been developed for their implementation. Eight open-source test programs, written in C language have been used for empirical evaluation of the regression testing approaches. Findings: The accuracy achieved by t-GSC, being a greedy technique, was found to be least; while that of ACO was found to be the best. All the tetrad approaches yielded borderline better or worse results, while all the four gave excellent time and size gains. Novelty: There are many studies available in the literature that compare various regression testing approaches of a similar kind. Instead of repeating the same, it is intended to evaluate two well-accepted approximation approaches: a hybrid approach, and a greedy approach. It has been tried to evaluate the efficiency of the greedy approach with the metaheuristic approach. It is imperative to compare approaches following different algorithmic paradigms, yet trying to solve the same problem.

Keywords: Ant Colony Optimization; Bee Colony Optimization; Genetic Algorithms; Greedy Set Cover; Software Testing; empirical comparison

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Copyright

© 2023 Singhal et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)