• Journal of KIISE
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• v.44 no.8
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• pp.832-841
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• 2017

Bug reports are essential documents for developers to localize and fix bugs. These reports contain information regarding software bugs or failures that occur during software operation and maintenance phase. Information Retrieval-based Bug Localization (IR-BL) techniques have been proposed to reduce the time and cost it takes for developers to resolve bug reports. However, if a low-quality bug report is submitted, the performance of such techniques can be significantly degraded. To address this problem, we propose a quality prediction method that selects low-quality bug reports. This process; defines a Quality property of a Bug report as a Query (Q4BaQ) and predicts the quality of the bug reports using machine learning. We evaluated the proposed method with 3 open source projects. The results of the experiment show that the proposed method achieved an average F-measure of 87.31% and outperformed previous prediction techniques by up to 6.62% in the F-measure. Finally, a combination of the proposed method and traditional automatic query reformulation method improved the MRR and MAP by 0.9% and 1.3%, respectively.