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| K. Shimonaka, S. Sumi, Y. Higo, and S. Kusumoto, "Identifying Auto-Generated Code by Using Machine Learning Techniques," In Kento Shimonaka, Soichi Sumi, Yoshiki Higo, Shinji Kusumoto, editor, Proc. of 7th International Workshop on Empirical Software Engineering in Practice (IWESEP), pp. 018-023, March 2016. | |
| ID | 448 |
| 分類 | 国際会議 |
| タグ | machine learning techniques auto-generated code software analysis |
| 表題 (title) |
Identifying Auto-Generated Code by Using Machine Learning Techniques |
| 表題 (英文) |
Identifying Auto-Generated Code by Using Machine Learning Techniques |
| 著者名 (author) |
Kento Shimonaka,Soichi Sumi,Yoshiki Higo,Shinji Kusumoto |
| 英文著者名 (author) |
Kento Shimonaka,Soichi Sumi,Yoshiki Higo,Shinji Kusumoto |
| 編者名 (editor) |
Kento Shimonaka, Soichi Sumi, Yoshiki Higo, Shinji Kusumoto |
| 編者名 (英文) |
Kento Shimonaka, Soichi Sumi, Yoshiki Higo, Shinji Kusumoto |
| キー (key) |
Kento Shimonaka,Soichi Sumi,Yoshiki Higo,Shinji Kusumoto |
| 書籍・会議録表題 (booktitle) |
Proc. of 7th International Workshop on Empirical Software Engineering in Practice (IWESEP) |
| 書籍・会議録表題(英文) |
Proc. of 7th International Workshop on Empirical Software Engineering in Practice (IWESEP) |
| 巻数 (volume) |
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| 号数 (number) |
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| ページ範囲 (pages) |
018-023 |
| 組織名 (organization) |
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| 出版元 (publisher) |
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| 出版元 (英文) |
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| 出版社住所 (address) |
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| 刊行月 (month) |
3 |
| 出版年 (year) |
2016 |
| 採択率 (acceptance) |
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| URL |
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| 付加情報 (note) |
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| 注釈 (annote) |
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| 内容梗概 (abstract) |
Recently, many researchers have conducted mining
source code repositories to retrieve useful information about software development. Source code repositories often include auto-generated code, and auto-generated code is usually removed in a preprocessing phase because the presence of auto-generated code is harmful to source code analysis. A usual way to remove auto-generated code is searching particular comments which exist among auto-generated code. However, we cannot identify auto-generated code automatically with such a way if comments have disappeared. In addition, it takes too much time to identify auto-generated code manually. Therefore, we propose a technique to identify auto-generated code automatically by using machine learning techniques. In our proposed technique, we can identify whether source code is auto-generated code or not by utilizing syntactic information of source code. In order to evaluate the proposed technique, we conducted experiments on source code generated by four kinds of code generators. As a result, we confirmed that the proposed technique was able to identify auto-generated code with high accuracy. |
| 論文電子ファイル | s-kento_iwesep2016_camera-ready_ver3.pdf (application/pdf) [一般閲覧可] |
| BiBTeXエントリ |
@inproceedings{id448,
title = {Identifying Auto-Generated Code by Using Machine Learning Techniques},
author = {Kento Shimonaka and Soichi Sumi and Yoshiki Higo and Shinji Kusumoto},
editor = {Kento Shimonaka, Soichi Sumi, Yoshiki Higo, Shinji Kusumoto},
booktitle = {Proc. of 7th International Workshop on Empirical Software Engineering in Practice (IWESEP)},
pages = {018-023},
month = {3},
year = {2016},
}
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