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Assessment of the Code Refactoring Dataset Regarding the Maintainability of Methods

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9789))

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Abstract

Code refactoring has a solid theoretical background while being used in development practice at the same time. However, previous works found controversial results on the nature of code refactoring activities in practice. Both their application context and impact on code quality needs further examination.

Our paper encourages the investigation of code refactorings in practice by providing an excessive open dataset of source code metrics and applied refactorings through several releases of 7 open-source systems. We already demonstrated the practical value of the dataset by analyzing the quality attributes of the refactored source code classes and the values of source code metrics improved by those refactorings.

In this paper, we have gone one step deeper and explored the effect of code refactorings at the level of methods. We found that similarly to class level, lower maintainability indeed triggers more code refactorings in practice at the level of methods and these refactorings significantly decrease size, coupling and clone metrics.

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Notes

  1. 1.

     http://www.sourcemeter.com/.

  2. 2.

     http://www.quality-gate.com/.

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Acknowledgment

This work was partially supported by the European Union project “REPARA – Reengineering and Enabling Performance And poweR of Applications”, project number: 609666.

Author information

Authors and Affiliations

  1. University of Szeged, Szeged, Hungary

    István Kádár, Péter Hegedűs, Rudolf Ferenc & Tibor Gyimóthy

Authors
  1. István Kádár
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  2. Péter Hegedűs
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  3. Rudolf Ferenc
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  4. Tibor Gyimóthy
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Corresponding author

Correspondence to Péter Hegedűs .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing Univ. of Posts & Telecomm., Beijing, China

    Shangguang Wang

Appendix

Appendix

Table 6. The type of refactorings extracted by RefFinder at class and method level
Full size table

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Kádár, I., Hegedűs, P., Ferenc, R., Gyimóthy, T. (2016). Assessment of the Code Refactoring Dataset Regarding the Maintainability of Methods. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_43

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  • DOI: https://doi.org/10.1007/978-3-319-42089-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42088-2

  • Online ISBN: 978-3-319-42089-9

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