IEICE TRANSACTIONS on Information
A Machine Learning Method for Automatic Copyright Notice Identification of Source Files
Summary :
For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.12 pp.2709-2712
- Publication Date
- 2020/12/01
- Publicized
- 2020/09/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8089
- Type of Manuscript
- LETTER
- Category
- Software Engineering
Authors
Shi QIU
Osaka University
German M. DANIEL
University of Victoria
Katsuro INOUE
Osaka University
Keyword
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Cite this
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Shi QIU, German M. DANIEL, Katsuro INOUE, "A Machine Learning Method for Automatic Copyright Notice Identification of Source Files" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 12, pp. 2709-2712, December 2020, doi: 10.1587/transinf.2020EDL8089.
Abstract: For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8089/_p
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@ARTICLE{e103-d_12_2709,
author={Shi QIU, German M. DANIEL, Katsuro INOUE, },
journal={IEICE TRANSACTIONS on Information},
title={A Machine Learning Method for Automatic Copyright Notice Identification of Source Files},
year={2020},
volume={E103-D},
number={12},
pages={2709-2712},
abstract={For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.},
keywords={},
doi={10.1587/transinf.2020EDL8089},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - A Machine Learning Method for Automatic Copyright Notice Identification of Source Files
T2 - IEICE TRANSACTIONS on Information
SP - 2709
EP - 2712
AU - Shi QIU
AU - German M. DANIEL
AU - Katsuro INOUE
PY - 2020
DO - 10.1587/transinf.2020EDL8089
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2020
AB - For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.
ER -
English
