›› 2012,Vol. ›› Issue (2): 397-412.doi: 10.1007/s11390-012-1230-3

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用于缺陷报告分配的缺陷优先级排序

Jaweria Kanwal and Onaiza Maqbool   

  • 收稿日期:2011-04-27 修回日期:2012-01-12 出版日期:2012-03-05 发布日期:2012-03-05

Bug Prioritization to Facilitate Bug Report Triage

Jaweria Kanwal and Onaiza Maqbool   

  1. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan
  • Received:2011-04-27 Revised:2012-01-12 Online:2012-03-05 Published:2012-03-05

软件系统缺陷库中大量新增缺陷报告的管理是一项具有挑战性的任务。手工处理这些报告费时且可能导致重要缺陷不能得到及时处理。针对该问题,人们开发出推荐工具,以自动给新缺陷报告进行优先级排序。本文提出并评价了一种基于分类的推荐工具开发方法。使用Naïve Bayes和支持向量机(Support Vector Machine,SVM),并针对准确性对多个分类器进行比较。由于一个缺陷报告包含分类和文本特征,我们进行的另外一项评价是根据组合特征能否更好地判定缺陷的优先级。为评价缺陷优先级推荐工具,我们使用了precision 和recall,并提出两种新的评价指标:Nearest False Negatives (NFN),Nearest False Positives (NFP)。我们发现使用文本特征时,SVM的结果优于Naïve Bayes算法,而对于分类特征,则相反;如果将文本和分类特征相组合,使用SVM进行分类,则性能最优。

Abstract: The large number of new bug reports received in bug repositories of software systems makes their management a challenging task. Handling these reports manually is time consuming, and often results in delaying the resolution of important bugs. To address this issue, a recommender may be developed which automatically prioritizes the new bug reports. In this paper, we propose and evaluate a classification based approach to build such a recommender. We use the Naïve Bayes and Support Vector Machine (SVM) classifiers, and present a comparison to evaluate which classifier performs better in terms of accuracy. Since a bug report contains both categorical and text features, another evaluation we perform is to determine the combination of features that better determines the priority of a bug. To evaluate the bug priority recommender, we use precision and recall measures and also propose two new measures, Nearest False Negatives (NFN) and Nearest False Positives (NFP), which provide insight into the results produced by precision and recall. Our findings are that the results of SVM are better than the Naïve Bayes algorithm for text features, whereas for categorical features, Naïve Bayes performance is better than SVM. The highest accuracy is achieved with SVM when categorical and text features are combined for training.

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