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conference paper · International Workshop on Empirical Software Engineering in Practice (IWESEP) · 2016

Identifying Auto-Generated Code by Using Machine Learning Techniques

Kento Shimonaka, Soichi Sumi, Yoshiki Higo, Shinji Kusumoto

Cited by 17Abstract and details only

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 removeauto-generated code is searching particular comments which existamong 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 techniqueto identify auto-generated code automatically by using machinelearning techniques. In our proposed technique, we can identifywhether source code is auto-generated code or not by utilizingsyntactic information of source code. In order to evaluate theproposed technique, we conducted experiments on source codegenerated by four kinds of code generators. As a result, weconfirmed that the proposed technique was able to identify auto-generated code with high accuracy.

Cite this paper

APA 7

Shimonaka, K., Sumi, S., Higo, Y., & Kusumoto, S. (2016). Identifying Auto-Generated Code by Using Machine Learning Techniques. International Workshop on Empirical Software Engineering in Practice (IWESEP), 18–23. https://doi.org/10.1109/iwesep.2016.18

MLA 9

Shimonaka, Kento, et al. “Identifying Auto-Generated Code by Using Machine Learning Techniques.” International Workshop on Empirical Software Engineering in Practice (IWESEP), 2016, pp. 18–23, https://doi.org/10.1109/iwesep.2016.18.

Chicago

Shimonaka, Kento, Soichi Sumi, Yoshiki Higo, and Shinji Kusumoto. 2016. “Identifying Auto-Generated Code by Using Machine Learning Techniques.” International Workshop on Empirical Software Engineering in Practice (IWESEP): 18–23. https://doi.org/10.1109/iwesep.2016.18.

Harvard

Shimonaka, K. et al. (2016) ‘Identifying Auto-Generated Code by Using Machine Learning Techniques’, International Workshop on Empirical Software Engineering in Practice (IWESEP), pp. 18–23. Available at: https://doi.org/10.1109/iwesep.2016.18.

BibTeX
@article{shimonaka2016identifying,
  title = {{Identifying Auto-Generated Code by Using Machine Learning Techniques}},
  author = {Shimonaka, Kento and Sumi, Soichi and Higo, Yoshiki and Kusumoto, Shinji},
  journal = {International Workshop on Empirical Software Engineering in Practice (IWESEP)},
  pages = {18--23},
  year = {2016},
  doi = {10.1109/iwesep.2016.18}
}

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