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Automating extract class refactoring: an improved method and its evaluation

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Abstract

During software evolution the internal structure of the system undergoes continuous modifications. These continuous changes push away the source code from its original design, often reducing its quality, including class cohesion. In this paper we propose a method for automating the Extract Class refactoring. The proposed approach analyzes (structural and semantic) relationships between the methods in a class to identify chains of strongly related methods. The identified method chains are used to define new classes with higher cohesion than the original class, while preserving the overall coupling between the new classes and the classes interacting with the original class. The proposed approach has been first assessed in an artificial scenario in order to calibrate the parameters of the approach. The data was also used to compare the new approach with previous work. Then it has been empirically evaluated on real Blobs from existing open source systems in order to assess how good and useful the proposed refactoring solutions are considered by software engineers and how well the proposed refactorings approximate refactorings done by the original developers. We found that the new approach outperforms a previously proposed approach and that developers find the proposed solutions useful in guiding refactorings.

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Notes

  1. These approaches do not provide support to extract class refactoring.

  2. http://www.eclipse.org/.

  3. http://www.jetbrains.com/idea/features/refactoring.html.

  4. If a private field needs to be shared by two or more of the extracted classes, the implementation of the needed getter and/or setter methods is left to the developer.

  5. It is worth noting that while the general experimental design is the same, the Max Flow-Min Cut approach (Bavota et al. 2011) was evaluated on artificial Blobs created merging only two classes, as it is only able to split a Blob in two classes.

  6. The complete results achieved with all possible combinations of parameters can be found in Bavota et al. (2012).

  7. The interested reader can find the interaction plots for all systems in our online appendix (Bavota et al. 2012).

  8. All students voluntarily took part to the study.

  9. In the number of methods we do not count the constructors (for both pre- and post-refactoring) and any getters and setters methods that would be added after the refactoring. In this way the sum of methods of the extracted classes is equals to the number of methods of the Blob class.

  10. The interested reader can find the results by the Max Flow-Min Cut approach for each Blob in our online appendix (Bavota et al. 2012).

  11. A fine grained analysis of the scores assigned by the students is reported in our online Appendix (Bavota et al. 2012).

  12. To avoid bias in the experiment none of the authors have been involved in this evaluation.

  13. None of the 50 students involved in the user study reported in Section 5 has been involved in this experiment.

  14. This data was provided by the subjects when sending their results to us.

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Acknowledgements

We would like to thank all the students who participated to our studies. We would also like to thank anonymous reviewers for their careful reading of our manuscript and high-quality feedback. Their detailed comments have helped us to substantially revise, extend, and improve the original version of this paper. Andrian Marcus was supported in part by grants from the US National Science Foundation (CCF-0845706 and CCF-1017263).

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Authors and Affiliations

  1. Software Engineering Lab, University of Salerno, Via ponte don Melillo, 84084, Fisciano, SA, Italy

    Gabriele Bavota & Andrea De Lucia

  2. SEVERE Group, Department of Computer Science, Wayne State University, 5057 Woodward Ave, Suite 14101.1, Detroit, MI, 48202, USA

    Andrian Marcus

  3. Department of Bioscience and Territory, University of Molise, C. da Fonte Lappone, 86090, Pesche, IS, Italy

    Rocco Oliveto

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  1. Gabriele Bavota
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  2. Andrea De Lucia
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  3. Andrian Marcus
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  4. Rocco Oliveto
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Correspondence to Rocco Oliveto.

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Communicated by: Arie van Deursen

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Bavota, G., De Lucia, A., Marcus, A. et al. Automating extract class refactoring: an improved method and its evaluation. Empir Software Eng 19, 1617–1664 (2014). https://doi.org/10.1007/s10664-013-9256-x

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