A Comparative Study of Automated Refactoring Tools
Researchers proposed several refactoring approaches supported by automated and semi-automated refactoring tools. However, the existence of numerous automated refactoring tools imposes difficulties on developers to decide upon the appropriate one according to their needs. Moreover, the performance of...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418470/ |
| _version_ | 1797319558387728384 |
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| author | Maha Alharbi Mohammad Alshayeb |
| author_facet | Maha Alharbi Mohammad Alshayeb |
| author_sort | Maha Alharbi |
| collection | DOAJ |
| description | Researchers proposed several refactoring approaches supported by automated and semi-automated refactoring tools. However, the existence of numerous automated refactoring tools imposes difficulties on developers to decide upon the appropriate one according to their needs. Moreover, the performance of the existing refactoring tools has not been empirically evaluated against the other available tools targeting the same refactoring opportunities. Therefore, the objective of this research is to conduct a comparative study to systematically compare and evaluate refactoring tools that belong to different categories of refactoring approaches. To this end, we propose an evaluation framework based on the DESMET methodology. The framework is used to empirically compare and evaluate four different refactoring tools, namely MultiRefactor, JDeodorant, jSparrow, and Spartenizer, using five open-source projects. The evaluation results show that jSparrow outperforms the other investigated tools by supporting the highest number of quantitative and qualitative features, suggesting that it is the best choice based on various perspectives. On the other hand, Spartenizer demonstrated the least favorable outcomes in terms of both quantitative and qualitative features, including introducing new code smells after applying a refactoring opportunity. The findings of this comparative study would assist the developers in understanding the characteristics and the capability of the studied refactoring tools. Also, it benefits the researchers to focus their efforts on addressing the identified limitations to enhance the smell detection and refactoring process. |
| first_indexed | 2024-03-08T04:08:43Z |
| format | Article |
| id | doaj.art-83c968033b2e41bf893a669d66011e4c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:08:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-83c968033b2e41bf893a669d66011e4c2024-02-09T00:03:04ZengIEEEIEEE Access2169-35362024-01-0112187641878110.1109/ACCESS.2024.336131410418470A Comparative Study of Automated Refactoring ToolsMaha Alharbi0https://orcid.org/0009-0007-2605-3011Mohammad Alshayeb1https://orcid.org/0000-0001-7950-0099Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaInformation and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaResearchers proposed several refactoring approaches supported by automated and semi-automated refactoring tools. However, the existence of numerous automated refactoring tools imposes difficulties on developers to decide upon the appropriate one according to their needs. Moreover, the performance of the existing refactoring tools has not been empirically evaluated against the other available tools targeting the same refactoring opportunities. Therefore, the objective of this research is to conduct a comparative study to systematically compare and evaluate refactoring tools that belong to different categories of refactoring approaches. To this end, we propose an evaluation framework based on the DESMET methodology. The framework is used to empirically compare and evaluate four different refactoring tools, namely MultiRefactor, JDeodorant, jSparrow, and Spartenizer, using five open-source projects. The evaluation results show that jSparrow outperforms the other investigated tools by supporting the highest number of quantitative and qualitative features, suggesting that it is the best choice based on various perspectives. On the other hand, Spartenizer demonstrated the least favorable outcomes in terms of both quantitative and qualitative features, including introducing new code smells after applying a refactoring opportunity. The findings of this comparative study would assist the developers in understanding the characteristics and the capability of the studied refactoring tools. Also, it benefits the researchers to focus their efforts on addressing the identified limitations to enhance the smell detection and refactoring process.https://ieeexplore.ieee.org/document/10418470/Comparative studyrefactoring approachesclustering-basedrule-basedsearch-basedtext-based |
| spellingShingle | Maha Alharbi Mohammad Alshayeb A Comparative Study of Automated Refactoring Tools IEEE Access Comparative study refactoring approaches clustering-based rule-based search-based text-based |
| title | A Comparative Study of Automated Refactoring Tools |
| title_full | A Comparative Study of Automated Refactoring Tools |
| title_fullStr | A Comparative Study of Automated Refactoring Tools |
| title_full_unstemmed | A Comparative Study of Automated Refactoring Tools |
| title_short | A Comparative Study of Automated Refactoring Tools |
| title_sort | comparative study of automated refactoring tools |
| topic | Comparative study refactoring approaches clustering-based rule-based search-based text-based |
| url | https://ieeexplore.ieee.org/document/10418470/ |
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