Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes arou...

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Autores principales:	Lakshminarayana P, SureshKumar T V
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	cuckoo search algorithm hybrid bee colony algorithm model-driven testing particle swarm optimization software testing uml diagrams Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/5e3df45bcbd3408085a04a725ca33d6a
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spelling	oai:doaj.org-article:5e3df45bcbd3408085a04a725ca33d6a2021-12-05T14:10:51ZAutomatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm2191-026X10.1515/jisys-2019-0051https://doaj.org/article/5e3df45bcbd3408085a04a725ca33d6a2020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0051https://doaj.org/toc/2191-026XSoftware testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).Lakshminarayana PSureshKumar T VDe Gruyterarticlecuckoo search algorithmhybrid bee colony algorithmmodel-driven testingparticle swarm optimizationsoftware testinguml diagramsScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 59-72 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	cuckoo search algorithm hybrid bee colony algorithm model-driven testing particle swarm optimization software testing uml diagrams Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	cuckoo search algorithm hybrid bee colony algorithm model-driven testing particle swarm optimization software testing uml diagrams Science Q Electronic computers. Computer science QA75.5-76.95 Lakshminarayana P SureshKumar T V Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
description	Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).
format	article
author	Lakshminarayana P SureshKumar T V
author_facet	Lakshminarayana P SureshKumar T V
author_sort	Lakshminarayana P
title	Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
title_short	Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
title_full	Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
title_fullStr	Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
title_full_unstemmed	Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm
title_sort	automatic generation and optimization of test case using hybrid cuckoo search and bee colony algorithm
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/5e3df45bcbd3408085a04a725ca33d6a
work_keys_str_mv	AT lakshminarayanap automaticgenerationandoptimizationoftestcaseusinghybridcuckoosearchandbeecolonyalgorithm AT sureshkumartv automaticgenerationandoptimizationoftestcaseusinghybridcuckoosearchandbeecolonyalgorithm
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Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

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