Constructing meaningful code changes via graph transformer
Abstract The rapid development of Open‐Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code change...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
|
| Series: | IET Software |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sfw2.12097 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558535902232576 |
|---|---|
| author | Shikai Guo Mengxuan Li Xin Ge Hui Li Rong Chen Tingting Li |
| author_facet | Shikai Guo Mengxuan Li Xin Ge Hui Li Rong Chen Tingting Li |
| author_sort | Shikai Guo |
| collection | DOAJ |
| description | Abstract The rapid development of Open‐Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long‐range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, a novel method is proposed, named Graph Transformer for learning meaningful Code Transformations (GTCT), to provide developers with preliminary and quick feedback when developers submit code changes, which can improve the quality of code changes and improve the efficiency of code review. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information of the source code limitation, the code graph embedding component captures the types and patterns of code changes by encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, the code transformation learning component uses the multi‐head attention mechanism and positional encoding mechanism to address the long‐range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code and others' taxonomy of code changes. |
| format | Article |
| id | doaj-art-739bb11a995d4194982a25cf6b6a7638 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-739bb11a995d4194982a25cf6b6a76382025-02-03T01:32:08ZengWileyIET Software1751-88061751-88142023-04-0117215416710.1049/sfw2.12097Constructing meaningful code changes via graph transformerShikai Guo0Mengxuan Li1Xin Ge2Hui Li3Rong Chen4Tingting Li5The College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun ChinaAbstract The rapid development of Open‐Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long‐range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, a novel method is proposed, named Graph Transformer for learning meaningful Code Transformations (GTCT), to provide developers with preliminary and quick feedback when developers submit code changes, which can improve the quality of code changes and improve the efficiency of code review. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information of the source code limitation, the code graph embedding component captures the types and patterns of code changes by encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, the code transformation learning component uses the multi‐head attention mechanism and positional encoding mechanism to address the long‐range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code and others' taxonomy of code changes.https://doi.org/10.1049/sfw2.12097software engineeringsoftware maintenance |
| spellingShingle | Shikai Guo Mengxuan Li Xin Ge Hui Li Rong Chen Tingting Li Constructing meaningful code changes via graph transformer IET Software software engineering software maintenance |
| title | Constructing meaningful code changes via graph transformer |
| title_full | Constructing meaningful code changes via graph transformer |
| title_fullStr | Constructing meaningful code changes via graph transformer |
| title_full_unstemmed | Constructing meaningful code changes via graph transformer |
| title_short | Constructing meaningful code changes via graph transformer |
| title_sort | constructing meaningful code changes via graph transformer |
| topic | software engineering software maintenance |
| url | https://doi.org/10.1049/sfw2.12097 |
| work_keys_str_mv | AT shikaiguo constructingmeaningfulcodechangesviagraphtransformer AT mengxuanli constructingmeaningfulcodechangesviagraphtransformer AT xinge constructingmeaningfulcodechangesviagraphtransformer AT huili constructingmeaningfulcodechangesviagraphtransformer AT rongchen constructingmeaningfulcodechangesviagraphtransformer AT tingtingli constructingmeaningfulcodechangesviagraphtransformer |