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Automatic Human-Like Detection of Code Smells

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Discovery Science (DS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12986))

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Abstract

Many code smell detection techniques and tools have been proposed, mainly aiming to eliminate design flaws and improve software quality. Most of them are based on heuristics which rely on a set of software metrics and corresponding threshold values. Those techniques and tools suffer from subjectivity issues, discordant results among the tools, and the reliability of the thresholds. To mitigate these problems, we used machine learning to automate developers’ perception in code smells detection. Different from other existing machine learning used in code smell detection we trained our models with an extensive dataset based on more than 3000 professional reviews on 518 open source projects. We conclude by an empirical evaluation of the performance of the machine learning approach against PMD, a widely used metric-based code smell detection tool for Java. The experimental results show that the machine learning approach outperforms the PMD classifier in all evaluations.

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Acknowledgements

We would like to thank Khon Kaen University for awarding KKU Outbound Visiting Scholarship to the first author.

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Authors and Affiliations

  1. Department of Computer Science, Khon Kaen University, Khon Kaen, Thailand

    Chitsutha Soomlek

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Jan N. van Rijn & Marcello M. Bonsangue

Authors
  1. Chitsutha Soomlek
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  2. Jan N. van Rijn
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  3. Marcello M. Bonsangue
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Corresponding author

Correspondence to Chitsutha Soomlek .

Editor information

Editors and Affiliations

  1. Universidade do Porto and Fraunhofer Portugal AICOS, Porto, Portugal

    Carlos Soares

  2. Dalhousie University, Halifax, NS, Canada

    Luis Torgo

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Soomlek, C., van Rijn, J.N., Bonsangue, M.M. (2021). Automatic Human-Like Detection of Code Smells. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-88942-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88941-8

  • Online ISBN: 978-3-030-88942-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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