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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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E93.D; no. 5; pp. 1154 - 1161
Main Authors CHEN, Ing-Xiang, LI, Chien-Hung, YANG, Cheng-Zen
Format Journal Article
LanguageEnglish
Published The Institute of Electronics, Information and Communication Engineers 2010
Subjects
bug localization
co-location shrinkage
Computer programs
fault-prone modules
Learning
Localization
Modules
Position (location)
Software
Source code
State of the art
statistical learning
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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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ISSN:0916-8532
1745-1361
1745-1361
DOI:10.1587/transinf.E93.D.1154