An Empirical Study of Source Code Detection Using Image Classification

Juntong Hong, Osamu Mizuno, Masanari Kondo

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

The detection of programming language for a source code file has achieved high accuracy using the machine learning techniques. On the other hand, for a piece of software (called snippet), the detection of programming language is required to append tags automatically in a question and answer site such as Stack Overflow. However, the detection of programming language for a snippet is still a challenge since snippets is not a complete source code. Usually, experienced developers can detect the language of such snippet at a glance. It is considered that such a task that a human being easily solves can be solved by the image classification method using deep learning technique. Therefore, we propose a programming language detection method using a deep learning based image classification method. By using the data from actual Q&A site, we evaluate our proposed model. The results of experiment demonstrate that we can successfully detect the correct programming language for snippets with over 90% accuracy.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
ISBN(電子版)9781728155906
DOI
出版ステータス出版済み - 12 2019
外部発表はい
イベント10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019 - Tokyo, 日本
継続期間: 12 13 201912 14 2019

出版物シリーズ

名前Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019

会議

会議10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
国/地域日本
CityTokyo
Period12/13/1912/14/19

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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