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Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization
The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regres...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227216/ https://www.ncbi.nlm.nih.gov/pubmed/35746156 http://dx.doi.org/10.3390/s22124374 |
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author | Bajaj, Anu Abraham, Ajith Ratnoo, Saroj Gabralla, Lubna Abdelkareim |
author_facet | Bajaj, Anu Abraham, Ajith Ratnoo, Saroj Gabralla, Lubna Abdelkareim |
author_sort | Bajaj, Anu |
collection | PubMed |
description | The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage. |
format | Online Article Text |
id | pubmed-9227216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92272162022-06-25 Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization Bajaj, Anu Abraham, Ajith Ratnoo, Saroj Gabralla, Lubna Abdelkareim Sensors (Basel) Article The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage. MDPI 2022-06-09 /pmc/articles/PMC9227216/ /pubmed/35746156 http://dx.doi.org/10.3390/s22124374 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bajaj, Anu Abraham, Ajith Ratnoo, Saroj Gabralla, Lubna Abdelkareim Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title | Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title_full | Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title_fullStr | Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title_full_unstemmed | Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title_short | Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization |
title_sort | test case prioritization, selection, and reduction using improved quantum-behaved particle swarm optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227216/ https://www.ncbi.nlm.nih.gov/pubmed/35746156 http://dx.doi.org/10.3390/s22124374 |
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