Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Ing-Xiang CHEN  Chien-Hung LI  Cheng-Zen YANG 

Publication
IEICE TRANSACTIONS on Information and Systems  Vol.E93-D  No.5  pp.1154-1161
Publication Date: 2010/05/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
Keyword: 
bug localization,  co-location shrinkage,  fault-prone modules,  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.