The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Para Software Livre e de Código Aberto (FOSS), é importante identificar os avisos de direitos autorais. No entanto, tanto a forma colaborativa de desenvolvimento de projetos FOSS quanto o grande número de arquivos fonte aumentam sua dificuldade. Neste artigo, pretendemos identificar automaticamente os avisos de direitos autorais em arquivos de origem com base em técnicas de aprendizado de máquina. O experimento de avaliação mostra que nosso método supera o FOSSology, o único método existente baseado em expressão regular.
Shi QIU
Osaka University
German M. DANIEL
University of Victoria
Katsuro INOUE
Osaka University
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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},}
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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 -