概要: 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. タグ: machine, learning, techniques, auto-generated, code, software, analysis

@inproceedings{KentoShimonaka2016,
  author = {Kento Shimonaka and Soichi Sumi and Yoshiki Higo and Shinji Kusumoto},
  title = {Identifying Auto-Generated Code by Using Machine Learning Techniques},
  booktitle = {Proc. of 7th International Workshop on Empirical Software Engineering in Practice (IWESEP)},
  pages = {018--023},
  year = {2016},
  month = {mar}
}