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Feature requests-based recommendation of software refactorings

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Abstract

Software requirements are ever-changing which often leads to software evolution. Consequently, throughout software lifetime, developers receive new requirements often expressed as feature requests. To implement the requested features, developers sometimes apply refactorings to make their systems adapt to the new requirements. However, deciding what refactorings to apply is often challenging and there is still lack of automated support to recommend refactorings given a feature request. To this end, we propose a learning-based approach that recommends refactorings based on the history of the previously requested features, applied refactorings, and code smells information. First, the state-of-the-art refactoring detection tools are leveraged to identify the previous refactorings applied to implement the past feature requests. Second, a machine classifier is trained with the history data of the feature requests, code smells, and refactorings applied on the respective commits. Consequently, the machine classifier is used to predict refactorings for new feature requests. The proposed approach is evaluated on the dataset of 55 open source Java projects and the results suggest that it can accurately recommend refactorings (accuracy is up to 83.19%).

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Acknowledgements

The authors would like to say thanks to the anonymous reviewers of this manuscript as well as to the program chairs and the anonymous reviewers of RE’19 conference for their insightful comments and constructive suggestions. This work is partially supported by the National Natural Science Foundation of China (No. 61772071 and No. 61690205), and the U.S. National Science Foundation Award (CCF-1350487).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Ally S. Nyamawe, Hui Liu, Qasim Umer & Zhendong Niu

  2. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA

    Nan Niu

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  1. Ally S. Nyamawe
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Correspondence to Hui Liu.

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Communicated by: Kelly Blincoe, Daniela Damian, and Anna Perini

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Nyamawe, A.S., Liu, H., Niu, N. et al. Feature requests-based recommendation of software refactorings. Empir Software Eng 25, 4315–4347 (2020). https://doi.org/10.1007/s10664-020-09871-2

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