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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A localização automatizada de bugs é uma questão importante na engenharia de software. Nas últimas décadas, várias abordagens de localização proativas e reativas foram propostas para prever os módulos de software propensos a falhas. No entanto, a maioria das abordagens proativas ou reativas precisam de informações de código-fonte ou métricas de complexidade de software para realizar a localização. Neste artigo, propomos uma abordagem reativa que considera apenas informações de relatórios de bugs e registros históricos de revisões. Em nossa abordagem, as relações de co-localização entre relatórios de bugs são exploradas para melhorar a precisão da previsão de um método de aprendizagem de última geração. Estudos sobre três projetos de código aberto revelam que o esquema proposto pode melhorar consistentemente a precisão da previsão em todos os três projetos de software em quase 11.6%, em média.
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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
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@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},}
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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 -