Bio-inspired metaheuristics: evolving and prioritizing software test data
Software testing is both a time and resource-consuming activity in software development. The most difficult parts of software testing are the generation and prioritization of test data. Principally these two parts are performed manually. Hence introducing an automation approach will significantly re...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 3; pp. 687 - 702 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-669X 1573-7497 |
| DOI | 10.1007/s10489-017-1003-3 |
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| Abstract | Software testing is both a time and resource-consuming activity in software development. The most difficult parts of software testing are the generation and prioritization of test data. Principally these two parts are performed manually. Hence introducing an automation approach will significantly reduce the total cost incurred in the software development lifecycle. A number of automatic test case generation (ATCG) and prioritization approaches have been explored. In this paper, we propose two approaches: (1) a pathspecific approach for ATCG using the following metaheuristic techniques: the genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony optimization (ABC); and (2) a test case prioritization (TCP) approach using PSO. Based on our experimental findings, we conclude that ABC outperforms the GA and PSO-based approaches for ATC.G Moreover, the results for PSO on TCP arguments demonstrate biased applicability for both small and large test suites against random, reverse and unordered prioritization schemes. Therefore, we focus on conducting a comprehensive and exhaustive study of the application of metaheuristic algorithms in solving ATCG and TCP problems in software engineering. |
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| AbstractList | Software testing is both a time and resource-consuming activity in software development. The most difficult parts of software testing are the generation and prioritization of test data. Principally these two parts are performed manually. Hence introducing an automation approach will significantly reduce the total cost incurred in the software development lifecycle. A number of automatic test case generation (ATCG) and prioritization approaches have been explored. In this paper, we propose two approaches: (1) a pathspecific approach for ATCG using the following metaheuristic techniques: the genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony optimization (ABC); and (2) a test case prioritization (TCP) approach using PSO. Based on our experimental findings, we conclude that ABC outperforms the GA and PSO-based approaches for ATC.G Moreover, the results for PSO on TCP arguments demonstrate biased applicability for both small and large test suites against random, reverse and unordered prioritization schemes. Therefore, we focus on conducting a comprehensive and exhaustive study of the application of metaheuristic algorithms in solving ATCG and TCP problems in software engineering. |
| Author | Mann, Mukesh Sangwan, Om Prakash Tomar, Pradeep |
| Author_xml | – sequence: 1 givenname: Mukesh orcidid: 0000-0003-4757-9710 surname: Mann fullname: Mann, Mukesh email: mukesh.gbu@gmail.com organization: Department of Computer Science and Engineering, School of Information & Communication Technology, Gautam Buddha University – sequence: 2 givenname: Pradeep surname: Tomar fullname: Tomar, Pradeep organization: Department of Computer Science and Engineering, School of Information & Communication Technology, Gautam Buddha University – sequence: 3 givenname: Om Prakash surname: Sangwan fullname: Sangwan, Om Prakash organization: Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology |
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| Keywords | Automatic test case generation Artificial bee colony Test case prioritization Genetic algorithm Particle swarm optimization |
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