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An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques

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Computer and Information Sciences II

Abstract

Automated maintenance of object-oriented software system designs via refactoring is a performance demanding combinatorial optimization problem. In this study, we made an empirical comparative study to see the performances of alternative search algorithms under a quality model defined by an aggregated software fitness metric. We handled 20 different refactoring actions that realize searches on design landscape defined by combination of 24 object-oriented software metrics. The investigated algorithms include random, steepest descent, multiple first descent, multiple steepest descent, simulated annealing and artificial bee colony searches. The study is realized by using a tool called A-CMA developed in Java that accepts bytecode compiled Java codes as its input. The empiricial study showed that multiple steepest descent and population-based artificial bee colony algorithms are two most suitable approaches for the efficient solution of the search based refactoring problem.

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Acknowledgements

The authors would like to thank Atilim University ARGEDA department for its financial support.

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Authors and Affiliations

  1. Department of Computer Engineering, Atilim University, Incek, Ankara, Turkey

    Ekin Koc, Nur Ersoy, Ali Andac, Zelal Seda Camlidere, Ibrahim Cereci & Hurevren Kilic

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  1. Ekin Koc
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  2. Nur Ersoy
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  3. Ali Andac
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  4. Zelal Seda Camlidere
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  5. Ibrahim Cereci
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  6. Hurevren Kilic
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Corresponding author

Correspondence to Hurevren Kilic .

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Editors and Affiliations

  1. , Dept of Electrical and Electronics Eng'g, Imperial College, London, SW7 2BT, United Kingdom

    Erol Gelenbe

  2. Imperial College, London, United Kingdom

    Ricardo Lent

  3. University of East London, London, United Kingdom

    Georgia Sakellari

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© 2011 Springer-Verlag London Limited

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Koc, E., Ersoy, N., Andac, A., Camlidere, Z.S., Cereci, I., Kilic, H. (2011). An Empirical Study About Search-Based Refactoring Using Alternative Multiple and Population-Based Search Techniques. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_7

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  • DOI: https://doi.org/10.1007/978-1-4471-2155-8_7

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2154-1

  • Online ISBN: 978-1-4471-2155-8

  • eBook Packages: EngineeringEngineering (R0)

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