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International Conference on Intelligent Computing

ICIC 2015: Intelligent Computing Theories and Methodologies pp 13–23Cite as

A Genetic Local Search Algorithm for Optimal Testing Resource Allocation in Module Software Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

Abstract

As modern software systems have been expanded continuously, the problem of how to optimally allocate the limited testing resource during the software testing phase attracted lots of attention. The Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking for an optimal allocation of limited testing resource. There are two major objectives in the OTRAPs: reliability and cost. Since the designers pay more and more attention to reducing the cost, in this paper, we studied OTRAPs with the latter objective. In previous work, approaches based on genetic algorithms have been claimed to be strong alternatives in solving the problem. Hence, in this paper we proposed a new algorithm based on genetic algorithm and local search strategy (GLSA) to solve the OTRAPs. Experimental results show that the algorithm proposed can obtain better performance than some existing approaches for solving the software testing resource problem.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, China

    Ruimin Gao & Siyan Xiong

Authors
  1. Ruimin Gao
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  2. Siyan Xiong
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Corresponding author

Correspondence to Ruimin Gao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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© 2015 Springer International Publishing Switzerland

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Gao, R., Xiong, S. (2015). A Genetic Local Search Algorithm for Optimal Testing Resource Allocation in Module Software Systems. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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