Cross-language refactoring detection method based on edit sequence
Aiming at the problems of unreliable commit message caused by developers not consistently recording refactoring operations, and language singularityin deep learning-based refactoring detection methods, a cross-language refactoring detection method RefCode was proposed. Firstly, refactoring collectio...
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| Format: | Article |
| Language: | zho |
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Hebei University of Science and Technology
2024-12-01
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| Series: | Journal of Hebei University of Science and Technology |
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| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406007?st=article_issue |
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| _version_ | 1841559994230636544 |
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| author | Tao LI Dongwen ZHANG Yang ZHANG Kun ZHENG |
| author_facet | Tao LI Dongwen ZHANG Yang ZHANG Kun ZHENG |
| author_sort | Tao LI |
| collection | DOAJ |
| description | Aiming at the problems of unreliable commit message caused by developers not consistently recording refactoring operations, and language singularityin deep learning-based refactoring detection methods, a cross-language refactoring detection method RefCode was proposed. Firstly, refactoring collection tools were employed to collect commit messages, code change information, and refactoring types from different programming languages, the edit sequences were generated from the code change information, and all the data were combined to create a dataset. Secondly, the CodeBERT pre-training model was combined with the BiLSTM-attention model to train and test on the dataset. Finally, the effectiveness of the proposed method was evaluated from six perspectives. The results show that RefCode achieves a significant improvement of about 50% in both precision and recall compared to the refactoring detection method which only uses commit messages as inputs to the LSTM model. The research results realize cross-language refactoring detection and effectively compensate for the defect of unreliable commit messages, which provides some reference for the detection of other programming languages and refactoring types. |
| format | Article |
| id | doaj-art-ad17a784f3924fdd9789900a4a6213a3 |
| institution | Kabale University |
| issn | 1008-1542 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj-art-ad17a784f3924fdd9789900a4a6213a32025-01-05T06:35:22ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422024-12-0145662763510.7535/hbkd.2024yx06007b202406007Cross-language refactoring detection method based on edit sequenceTao LI0Dongwen ZHANG1Yang ZHANG2Kun ZHENG3School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaAiming at the problems of unreliable commit message caused by developers not consistently recording refactoring operations, and language singularityin deep learning-based refactoring detection methods, a cross-language refactoring detection method RefCode was proposed. Firstly, refactoring collection tools were employed to collect commit messages, code change information, and refactoring types from different programming languages, the edit sequences were generated from the code change information, and all the data were combined to create a dataset. Secondly, the CodeBERT pre-training model was combined with the BiLSTM-attention model to train and test on the dataset. Finally, the effectiveness of the proposed method was evaluated from six perspectives. The results show that RefCode achieves a significant improvement of about 50% in both precision and recall compared to the refactoring detection method which only uses commit messages as inputs to the LSTM model. The research results realize cross-language refactoring detection and effectively compensate for the defect of unreliable commit messages, which provides some reference for the detection of other programming languages and refactoring types.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406007?st=article_issuesoftware engineering; refactoring detection; deep learning; cross-language; code change; edit sequence |
| spellingShingle | Tao LI Dongwen ZHANG Yang ZHANG Kun ZHENG Cross-language refactoring detection method based on edit sequence Journal of Hebei University of Science and Technology software engineering; refactoring detection; deep learning; cross-language; code change; edit sequence |
| title | Cross-language refactoring detection method based on edit sequence |
| title_full | Cross-language refactoring detection method based on edit sequence |
| title_fullStr | Cross-language refactoring detection method based on edit sequence |
| title_full_unstemmed | Cross-language refactoring detection method based on edit sequence |
| title_short | Cross-language refactoring detection method based on edit sequence |
| title_sort | cross language refactoring detection method based on edit sequence |
| topic | software engineering; refactoring detection; deep learning; cross-language; code change; edit sequence |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406007?st=article_issue |
| work_keys_str_mv | AT taoli crosslanguagerefactoringdetectionmethodbasedoneditsequence AT dongwenzhang crosslanguagerefactoringdetectionmethodbasedoneditsequence AT yangzhang crosslanguagerefactoringdetectionmethodbasedoneditsequence AT kunzheng crosslanguagerefactoringdetectionmethodbasedoneditsequence |