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A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava

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Search Based Software Engineering (SSBSE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10452))

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Abstract

Integration testing seeks to find communication problems between different units of a software system. As the order in which units are considered can impact the overall effort required to perform integration testing, deciding an appropriate sequence to integrate and test units is vital. Here we apply a multi-objective hyper-heuristic set within an NSGA-II framework to the Integration and Test Order Problem (ITO) for Google Guava, a set of open-source common libraries for Java. Our results show that an NSGA-II based hyper-heuristic employing a simplified version of Choice Function heuristic selection, outperforms standard NSGA-II for this problem.

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References

  1. google/guava: Google Core Libraries for Java. https://github.com/google/guava. Accessed: 25 Apr 2017

  2. Assunção, W.K.G., Colanzi, T.E., Vergilio, S.R., Pozo, A.: A multi-objective optimization approach for the integration and test order problem. Inf. Sci. 267, 119–139 (2014)

    Article  MathSciNet  Google Scholar 

  3. Coello, C.C., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems. Genetic and Evolutionary Computation, 2nd edn. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  4. Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001). doi:10.1007/3-540-44629-X_11

    Chapter  Google Scholar 

  5. Guizzo, G., Fritsche, G.M., Vergilio, S.R., Pozo, A.T.R.: A hyper-heuristic for the multi-objective integration and test order problem. In: Proceedings of GECCO 2015, pp. 1343–1350. ACM (2015)

    Google Scholar 

  6. Guizzo, G., Vergilio, S.R., Pozo, A.T.: Evaluating a multi-objective hyper-heuristic for the integration and test order problem. In: 2015 Brazilian Conference on Intelligent Systems (BRACIS), pp. 1–6. IEEE (2015)

    Google Scholar 

  7. Guizzo, G., Vergilio, S.R., Pozo, A.T., Fritsche, G.M.: A multi-objective and evolutionary hyper-heuristic applied to the integration and test order problem. Appl. Soft Comput. 56, 331–344 (2017)

    Article  Google Scholar 

  8. Scitools: Understand. https://scitools.com/features/. Accessed 25 Apr 2017

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Author information

Authors and Affiliations

  1. Federal University of Paraná (UFPR), Curitiba, Brazil

    Giovani Guizzo

  2. Operational Research Group, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    Mosab Bazargani & John H. Drake

  3. CREST, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Matheus Paixao

Authors
  1. Giovani Guizzo
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  2. Mosab Bazargani
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  3. Matheus Paixao
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  4. John H. Drake
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Corresponding author

Correspondence to Giovani Guizzo .

Editor information

Editors and Affiliations

  1. North Carolina State University, Morgantown, West Virginia, USA

    Tim Menzies

  2. Department of Computer Science, University College London, London, United Kingdom

    Justyna Petke

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Guizzo, G., Bazargani, M., Paixao, M., Drake, J.H. (2017). A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava. In: Menzies, T., Petke, J. (eds) Search Based Software Engineering. SSBSE 2017. Lecture Notes in Computer Science(), vol 10452. Springer, Cham. https://doi.org/10.1007/978-3-319-66299-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-66299-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66298-5

  • Online ISBN: 978-3-319-66299-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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