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Code smells detection via modern code review: a study of the OpenStack and Qt communities

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Abstract

Code review plays an important role in software quality control. A typical review process involves a careful check of a piece of code in an attempt to detect and locate defects and other quality issues/violations. One type of issue that may impact the quality of software is code smells - i.e., bad coding practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in modern code review and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews by analysing reviews from four, large open source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We manually checked a total of 25,415 code review comments obtained by keywords search and random selection; this resulted in the identification of 1,539 smell-related reviews which then allowed the study of the causes of code smells, actions taken against identified smells, time taken to fix identified smells and reasons why developers ignored fixing identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, 4) developers generally followed those recommendations and actioned the changes, 5) once identified by reviewers, it usually takes developers less than one week to fix the smells and 6) the main reason why developers chose to ignore the identified smells is that it is not worth fixing the smell. Our results suggest the following: 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project’s development team) and 3) program context needs to be fully considered in order to make a decision of whether to fix the identified code smell immediately.

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Notes

  1. https://pmd.github.io

  2. https://www.sonarqube.org

  3. https://www.designite-tools.com

  4. https://www.gerritcodereview.com

  5. https://www.openstack.org

  6. https://wiki.openstack.org/wiki/Nova

  7. https://wiki.openstack.org/wiki/Neutron

  8. https://www.qt.io/

  9. https://github.com/qt/qtbase

  10. https://www.qt.io/product/development-tools

  11. As of March 2022: https://www.openhub.net/p/openstack

  12. https://www.gerritcodereview.com

  13. Implemented using the NLTK package: https://www.nltk.org

  14. http://alturl.com/gqn7u

  15. http://alturl.com/2kcko

  16. http://alturl.com/4s775

  17. http://alturl.com/786zn

  18. https://www.maxqda.com/

  19. http://alturl.com/b2dkt

  20. http://alturl.com/odfjv

  21. http://alturl.com/azijc

  22. http://alturl.com/h2bpc

  23. http://alturl.com/cisgv

  24. http://alturl.com/y2ndw

  25. https://scipy.org/

  26. http://alturl.com/rrxo7

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 62172311 and the Special Fund of Hubei Luojia Laboratory.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Xiaofeng Han, Peng Liang, Bing Li & Yajing Luo

  2. Hubei Luojia Laboratory, Wuhan, China

    Xiaofeng Han, Peng Liang, Bing Li & Yajing Luo

  3. School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand

    Amjed Tahir

  4. Department of Computer Science, Brunel University London, London, UK

    Steve Counsell

  5. Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland, New Zealand

    Kelly Blincoe

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  1. Xiaofeng Han
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Correspondence to Peng Liang.

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Communicated by: Anita Sarma, Fabio Palomba, Alexander Serebrenik

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Han, X., Tahir, A., Liang, P. et al. Code smells detection via modern code review: a study of the OpenStack and Qt communities. Empir Software Eng 27, 127 (2022). https://doi.org/10.1007/s10664-022-10178-7

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