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Evolutionary Approaches to Test Data Generation for Object-Oriented Software: Overview of Techniques and Tools

Evolutionary Approaches to Test Data Generation for Object-Oriented Software: Overview of Techniques and Tools

Ana Filipa Nogueira, José Carlos Bregieiro Ribeiro, Francisco Fernández de Vega, Mário Alberto Zenha-Rela
Copyright: © 2018 |Pages: 33
ISBN13: 9781522550204|ISBN10: 1522550208|EISBN13: 9781522550211
DOI: 10.4018/978-1-5225-5020-4.ch006
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MLA

Nogueira, Ana Filipa, et al. "Evolutionary Approaches to Test Data Generation for Object-Oriented Software: Overview of Techniques and Tools." Incorporating Nature-Inspired Paradigms in Computational Applications, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 162-194. https://doi.org/10.4018/978-1-5225-5020-4.ch006

APA

Nogueira, A. F., Ribeiro, J. C., Fernández de Vega, F., & Zenha-Rela, M. A. (2018). Evolutionary Approaches to Test Data Generation for Object-Oriented Software: Overview of Techniques and Tools. In M. Khosrow-Pour, D.B.A. (Ed.), Incorporating Nature-Inspired Paradigms in Computational Applications (pp. 162-194). IGI Global. https://doi.org/10.4018/978-1-5225-5020-4.ch006

Chicago

Nogueira, Ana Filipa, et al. "Evolutionary Approaches to Test Data Generation for Object-Oriented Software: Overview of Techniques and Tools." In Incorporating Nature-Inspired Paradigms in Computational Applications, edited by Mehdi Khosrow-Pour, D.B.A., 162-194. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5020-4.ch006

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Abstract

In object-oriented evolutionary testing, metaheuristics are employed to select or generate test data for object-oriented software. Techniques that analyse program structures are predominant among the panoply of studies available in current literature. For object-oriented evolutionary testing, the common objective is to reach some coverage criteria, usually in the form of statement or branch coverage. This chapter explores, reviews, and contextualizes relevant literature, tools, and techniques in this area, while identifying open problems and setting ground for future work.

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