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International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 418–427Cite as

Code Smells Enabled by Artificial Intelligence: A Systematic Mapping

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11622))

Abstract

Code smells are an indicator of poor design in software systems. Artificial intelligence techniques have been applied in several ways to improve soft-ware quality in code smells detection i.e. (detection rules or standards using a combination of object-oriented metrics and Bayesian inference graphs). Literature in the field has identified artificial intelligence techniques and compare different artificial intelligence algorithms, which are used in the detection of code smells. However, to the best of our knowledge, there is not a systematic literature review devoted to study in deep the interaction of these fields. In this paper, authors conduct a systematic mapping to get to know how artificial intelligence inter-acts with code smells. Results show the deep connection of Artificial Intelligence with code smells in a solid way, as well as, providing potential challenges and opportunities for future research.

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Author information

Authors and Affiliations

  1. Faculty of Computer Sciences, Østfold University College, Postboks 700, 1757, Halden, Norway

    Moayid Ali Zaidi & Ricardo Colomo-Palacios

Authors
  1. Moayid Ali Zaidi
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  2. Ricardo Colomo-Palacios
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Corresponding author

Correspondence to Ricardo Colomo-Palacios .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Zaidi, M.A., Colomo-Palacios, R. (2019). Code Smells Enabled by Artificial Intelligence: A Systematic Mapping. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_31

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  • DOI: https://doi.org/10.1007/978-3-030-24305-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24304-3

  • Online ISBN: 978-3-030-24305-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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