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Test Suite Prioritization Using Nature Inspired Meta-Heuristic Algorithms

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Real World is filled with various hard and complex problems. One such complex problem is an optimization problem. Optimization has been an active area of research for several decades. Optimized solutions are hard to find so there are no deterministic algorithms that can find exact solution in polynomial time. In large domain of applications of intelligence techniques we are interested in exploring the application of Biographical Based Optimization (BBO) and Grey Wolf Optimizer (GWO) meta-heuristic algorithm to the domain of software testing. The GWO mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In this paper we adapt BBO and GWO for test suite prioritization and minimization and evaluate their performance with other nature inspired meta-heuristics.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University (Formerly DCE), Delhi, India

    Daya Gupta & Vishal Gupta

Authors
  1. Daya Gupta
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  2. Vishal Gupta
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Corresponding author

Correspondence to Vishal Gupta .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Gupta, D., Gupta, V. (2017). Test Suite Prioritization Using Nature Inspired Meta-Heuristic Algorithms. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

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