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Computational Intelligence and Quantitative Software Engineering pp 89–115Cite as

Metaheuristic Optimisation and Mutation-Driven Test Data Generation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 617))

Abstract

Metaheuristic optimisation techniques can be used in combination with mutation analysis to generate test data that is effective at finding faults and reduces the human effort involved in software testing. This chapter describes and evaluates various different metaheuristic techniques and considers their underlying properties in relation to test data generation. This represents the first attempt to bring together, compare and review ideas and research related to mutation analysis and metaheuristic optimisation. The intention is that by considering these application areas together, we can appreciate and understand important aspects of their strengths and weaknesses. This will allow us to make suggestions with regards to the ways in which they may be used together for maximum effectiveness and efficiency.

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Authors and Affiliations

  1. Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK

    Matthew Patrick

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  1. Matthew Patrick
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Correspondence to Matthew Patrick .

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  1. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Innopolis University, Bolzano, Italy

    Giancarlo Succi

  3. Center for Applied Software Engineering, Bolzano, Italy

    Alberto Sillitti

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Patrick, M. (2016). Metaheuristic Optimisation and Mutation-Driven Test Data Generation. In: Pedrycz, W., Succi, G., Sillitti, A. (eds) Computational Intelligence and Quantitative Software Engineering. Studies in Computational Intelligence, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-319-25964-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-25964-2_5

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  • Online ISBN: 978-3-319-25964-2

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