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)
号数 (number)
ページ範囲 (pages) 018-023
組織名 (organization)
出版元 (publisher)
出版元 (英文)
出版社住所 (address)
刊行月 (month) 3
出版年 (year) 2016
採択率 (acceptance)
URL
付加情報 (note)
注釈 (annote)
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},
}