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论文类型：期刊论文

发表时间：2018-04-01

发表刊物：INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING

收录刊物：SCIE、Scopus

卷号：28

期号：4,SI

页面范围：537-558

ISSN号：0218-1940

关键字：Bug report; severity prediction; machine learning; feature selection

摘要：In software maintenance process, it is a fairly important activity to predict the severity of bug reports. However, manually identifying the severity of bug reports is a tedious and time-consuming task. So developing automatic judgment methods for predicting the severity of bug reports has become an urgent demand. In general, a bug report contains a lot of descriptive natural language texts, thus resulting in a high-dimensional feature set which poses serious challenges to traditionally automatic methods. Therefore, we attempt to use automatic feature selection methods to improve the performance of the severity prediction of bug reports. In this paper, we introduce a ranking-based strategy to improve existing feature selection algorithms and propose an ensemble feature selection algorithm by combining existing ones. In order to verify the performance of our method, we run experiments over the bug reports of Eclipse and Mozilla and conduct comparisons with eight commonly used feature selection methods. The experiment results show that the ranking-based strategy can effectively improve the performance of the severity prediction of bug reports by up to 54.76% on average in terms of F-measure, and it also can significantly reduce the dimension of the feature set. Meanwhile, the ensemble feature selection method can get better results than a single feature selection algorithm.