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Ranking code clones to support maintenance activities

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Abstract

Developers often reuse code fragments by copy-and-paste activities to speed up code delivery. Through this copy-and-paste process, they create duplicated code, also known as code clones. As the software system evolves, the number of clones can increase substantially and impact code quality negatively. Prior studies have shown that inconsistent changes on code clones can introduce bugs in a software system and clones that have experienced some specific evolutionary patterns being more at risk than others. As the number of clone copies increases in a software system. it becomes tedious and time-consuming for developers to track and maintain all code clones. Recent studies have proposed approaches to analyze the clone evolution history for better clone maintenance. However, these approaches do not provide a specified list of code clones at a granular level (i.e., commits) that can help developers prioritize their clone maintenance activities. It is important to track the code clone changes at the commit level, as developers can fix/refactor code clones early. In this paper, we leverage machine learning to develop clone ranking models that can help developers identify the most risky clones early on. Specifically, we detect clones from 52 projects (34 Java and 18 C) that have 534,672 commits and build 469,239 clone genealogies. We extract 28 features capturing the characteristics of code clones at commit level. We then train learning-to-rank (LtR), classification, and regression machine learning models to rank the code clones based on fault occurrence during their evolutionary history. Our comparison of machine learning approaches indicates that classification (for the probability of being faulty) and regression (for the proportion of faulty changes) perform well in ranking code clones. Multiple unique developers who change a code clone and the age of a code clone (in terms of the number of cloned code changes) have a significant effect on the risk of faults in the code clones. Our results can help developers identify the most risky code clones first and prioritize them for refactoring to prevent future faults.

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Notes

  1. https://github.com/deeplearning4j/nd4j

  2. https://github.com/eclipse/deeplearning4j/commit/0ec1c8f

  3. https://ghtorrent.org/gcloud.html

  4. https://github.com/artem-solovev/gloc

  5. https://github.com/dmlc/xgboost

  6. https://github.com/microsoft/LightGBM

  7. https://zenodo.org/record/7229977

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada

    Osama Ehsan & Foutse Khomh

  2. Polytechnique Montréal, Montréal, QC, Canada

    Ying Zou

  3. Huawei Technologies Co., Foshan, Guangdong, China

    Dong Qiu

Authors
  1. Osama Ehsan
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  2. Foutse Khomh
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  3. Ying Zou
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  4. Dong Qiu
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Corresponding author

Correspondence to Osama Ehsan.

Additional information

Communicated by: Miryung Kim

Data availability statement

The datasets generated during and/or analysed during the current study are available in the Zenodo repositoryFootnote 7.

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Appendix

Appendix

Table 6 Details of the selected projects
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Table 7 Results of the mixed-effect model for Modelall, Sorted by χ2 descendingly
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Table 8 Results of the mixed-effect model for Modelmature, Sorted by χ2 descendingly
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Table 9 Results of the mixed-effect model for Modelearly, Sorted by χ2 descendingly
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Ehsan, O., Khomh, F., Zou, Y. et al. Ranking code clones to support maintenance activities. Empir Software Eng 28, 70 (2023). https://doi.org/10.1007/s10664-023-10292-0

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