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
Penyetempatan pepijat automatik adalah isu penting dalam kejuruteraan perisian. Dalam beberapa dekad yang lalu, pelbagai pendekatan penyetempatan proaktif dan reaktif telah dicadangkan untuk meramalkan modul perisian yang terdedah kepada kerosakan. Walau bagaimanapun, kebanyakan pendekatan proaktif atau reaktif memerlukan maklumat kod sumber atau metrik kerumitan perisian untuk melaksanakan penyetempatan. Dalam makalah ini, kami mencadangkan pendekatan reaktif yang hanya mempertimbangkan maklumat laporan pepijat dan log semakan sejarah. Dalam pendekatan kami, hubungan lokasi bersama antara laporan pepijat diterokai untuk meningkatkan ketepatan ramalan kaedah pembelajaran terkini. Kajian ke atas tiga projek sumber terbuka mendedahkan bahawa skim yang dicadangkan boleh meningkatkan ketepatan ramalan secara konsisten dalam ketiga-tiga projek perisian sebanyak hampir 11.6% secara purata.
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Salinan
Ing-Xiang CHEN, Chien-Hung LI, Cheng-Zen YANG, "Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 5, pp. 1154-1161, May 2010, doi: 10.1587/transinf.E93.D.1154.
Abstract: Automated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the co-location relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1154/_p
Salinan
@ARTICLE{e93-d_5_1154,
author={Ing-Xiang CHEN, Chien-Hung LI, Cheng-Zen YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules},
year={2010},
volume={E93-D},
number={5},
pages={1154-1161},
abstract={Automated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the co-location relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.},
keywords={},
doi={10.1587/transinf.E93.D.1154},
ISSN={1745-1361},
month={May},}
Salinan
TY - JOUR
TI - Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules
T2 - IEICE TRANSACTIONS on Information
SP - 1154
EP - 1161
AU - Ing-Xiang CHEN
AU - Chien-Hung LI
AU - Cheng-Zen YANG
PY - 2010
DO - 10.1587/transinf.E93.D.1154
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2010
AB - Automated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the co-location relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.
ER -