Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules
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 sof...
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Published in | IEICE Transactions on Information and Systems Vol. E93.D; no. 5; pp. 1154 - 1161 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
The Institute of Electronics, Information and Communication Engineers
2010
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0916-8532 1745-1361 1745-1361 |
DOI: | 10.1587/transinf.E93.D.1154 |