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MIRROR: multi-objective refactoring recommendation via correlation analysis

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Abstract

Refactoring is a critical but complex process to improve code quality by altering software structure without changing the observable behavior. Search-based approaches have been proposed to recommend refactoring solutions. However, existing works tend to leverage all the sub-attributes in an objective and ignore the relationship between the sub-attributes. Furthermore, the types of refactoring operations in the existing works can be further augmented. To this end, this paper proposes a novel approach, called MIRROR, to recommend refactoring by employing a multi-objective optimization across three objectives: (i) improving quality, (ii) removing code smell, and (iii) maximizing the similarity to refactoring history. Unlike previous works, MIRROR provides a way to further optimize attributes in each objective. To be more specific, given an objective, MIRROR investigates the possible correlations among attributes and selects those attributes with low correlations as the representation of this objective. MIRROR is evaluated on 6 real-world projects by answering 6 research questions. The experimental results demonstrate that MIRROR recommends an average of 43 solutions for each project. Furthermore, we compare MIRROR against existing tools JMove and QMove, and show that the F1 of MIRROR is 5.63% and 3.75% higher than that of JMove and QMove, demonstrating the effectiveness of MIRROR.

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Data availability

The data generated during and/or analyzed during the current study are available at https://uzhangyang.github.io/research/mirror.html.

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Acknowledgements

The authors would like to thank the insightful comments and suggestions of those anonymous reviewers, which have improved the presentation. This work is partially supported by the Hebei Natural Science Foundation under Grant No.F2023208001, the Hebei Oversea High-level Talent Foundation under Grant No.C20230358, and the Hebei Graduate Innovation Project under Grant No. CXZZSS2022081.

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  1. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, China

    Yang Zhang, Ke Guan & Lining Fang

  2. Hebei Technology Innovation Center of Intelligent IoT, Shijiazhuang, Hebei, China

    Yang Zhang

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Y.Z.: Proposing the idea, experimental analysis, writing—review & editing. K.G.: conducting experimentation, writing—original draft, review & editing. L.F.: conducting experimentation, methodology, writing—original draft.

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Zhang, Y., Guan, K. & Fang, L. MIRROR: multi-objective refactoring recommendation via correlation analysis. Autom Softw Eng 31, 2 (2024). https://doi.org/10.1007/s10515-023-00400-1

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