Where Should We Fix This Bug? A Two-Phase Recommendation Model

被引：139

作者：

Kim, Dongsun ^{[1
]}

Tao, Yida ^{[1
]}

Kim, Sunghun ^{[1
]}

Zeller, Andreas ^{[2
]}

机构：

[1] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China

[2] Univ Saarlandes Informat, D-66123 Saarbrucken, Germany

来源：

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | 2013年 / 39卷 / 11期

关键词：

Bug reports; machine learning; patch file prediction; PROBABILISTIC RANKING; PREDICTING FAULTS; IMPACT ANALYSIS; ERRORS; TOOL;

D O I：

10.1109/TSE.2013.24

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

To support developers in debugging and locating bugs, we propose a two-phase prediction model that uses bug reports' contents to suggest the files likely to be fixed. In the first phase, our model checks whether the given bug report contains sufficient information for prediction. If so, the model proceeds to predict files to be fixed, based on the content of the bug report. In other words, our two-phase model "speaks up" only if it is confident of making a suggestion for the given bug report; otherwise, it remains silent. In the evaluation on the Mozilla "Firefox" and "Core" packages, the two-phase model was able to make predictions for almost half of all bug reports; on average, 70 percent of these predictions pointed to the correct files. In addition, we compared the two-phase model with three other prediction models: the Usual Suspects, the one-phase model, and BugScout. The two-phase model manifests the best prediction performance.

引用

页码：1597 / 1610

页数：14

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