A Survey of Automatic Source Code Summarization
Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code sum...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/3/471 |
| _version_ | 1797441651369574400 |
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| author | Chunyan Zhang Junchao Wang Qinglei Zhou Ting Xu Ke Tang Hairen Gui Fudong Liu |
| author_facet | Chunyan Zhang Junchao Wang Qinglei Zhou Ting Xu Ke Tang Hairen Gui Fudong Liu |
| author_sort | Chunyan Zhang |
| collection | DOAJ |
| description | Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code summarization is mismatched with the source code, missing, or out of date. Manual source code summarization is inefficient and requires a lot of human efforts. To overcome such situations, many studies have been conducted on Automatic Source Code Summarization (ASCS). Given a set of source code, the ASCS techniques can automatically generate a summary described with natural language. In this paper, we give a review of the development of ASCS technology. Almost all ASCS technology involves the following stages: source code modeling, code summarization generation, and quality evaluation. We further categorize the existing ASCS techniques based on the above stages and analyze their advantages and shortcomings. We also draw a clear map on the development of the existing algorithms. |
| first_indexed | 2024-03-09T12:26:14Z |
| format | Article |
| id | doaj.art-e47818284d044a68a0d8df9b3065df28 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T12:26:14Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-e47818284d044a68a0d8df9b3065df282023-11-30T22:35:05ZengMDPI AGSymmetry2073-89942022-02-0114347110.3390/sym14030471A Survey of Automatic Source Code SummarizationChunyan Zhang0Junchao Wang1Qinglei Zhou2Ting Xu3Ke Tang4Hairen Gui5Fudong Liu6State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaSchool of Information Engineering, ZhengZhou University, Zhengzhou 450001, ChinaSchool of Information Engineering, ZhengZhou University, Zhengzhou 450001, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaSource code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code summarization is mismatched with the source code, missing, or out of date. Manual source code summarization is inefficient and requires a lot of human efforts. To overcome such situations, many studies have been conducted on Automatic Source Code Summarization (ASCS). Given a set of source code, the ASCS techniques can automatically generate a summary described with natural language. In this paper, we give a review of the development of ASCS technology. Almost all ASCS technology involves the following stages: source code modeling, code summarization generation, and quality evaluation. We further categorize the existing ASCS techniques based on the above stages and analyze their advantages and shortcomings. We also draw a clear map on the development of the existing algorithms.https://www.mdpi.com/2073-8994/14/3/471source code summarizationdeep learningprogram analysisneural machine translation |
| spellingShingle | Chunyan Zhang Junchao Wang Qinglei Zhou Ting Xu Ke Tang Hairen Gui Fudong Liu A Survey of Automatic Source Code Summarization Symmetry source code summarization deep learning program analysis neural machine translation |
| title | A Survey of Automatic Source Code Summarization |
| title_full | A Survey of Automatic Source Code Summarization |
| title_fullStr | A Survey of Automatic Source Code Summarization |
| title_full_unstemmed | A Survey of Automatic Source Code Summarization |
| title_short | A Survey of Automatic Source Code Summarization |
| title_sort | survey of automatic source code summarization |
| topic | source code summarization deep learning program analysis neural machine translation |
| url | https://www.mdpi.com/2073-8994/14/3/471 |
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