Identifying and predicting key features to support bug reporting
Identifying and predicting key features to support bug reporting
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DOI：
10.1002/smr.2184
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
M. Karim;Akinori Ihara;Eunjong Choi;Hajimu Iida
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文献类型：
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作者：
M. Karim;Akinori Ihara;Eunjong Choi;Hajimu Iida
错误报告是开发人员分类和修复错误的主要手段，我们首先进行探索性研究，以确定记者在其最初的错误报告中经常错过的关键功能。预测记者是否应提供某些关键功能，以确保骆驼，德比，检票口，Firefox和Thunderbird项目的错误报告。预期的行为是记者最初从其初始错误报告中省略的其他功能。流程在我们的发现的基础上，我们使用四种不同的文本分类技术来评估和评估历史错误的知识，以预测关键特征对不同文本分类技术的研究表明，幼稚的贝叶斯多项式（NBM）的表现要优于其他技术。错误报告的内容。
Bug reports are the primary means through which developers triage and fix bugs. To achieve this effectively, bug reports need to clearly describe those features that are important for the developers. However, previous studies have found that reporters do not always provide such features. Therefore, we first perform an exploratory study to identify the key features that reporters frequently miss in their initial bug report submissions. Then, we propose an approach that predicts whether reporters should provide certain key features to ensure a good bug report. A case study of the bug reports for Camel, Derby, Wicket, Firefox, and Thunderbird projects shows that Steps to Reproduce, Test Case, Code Example, Stack Trace, and Expected Behavior are the additional features that reporters most often omit from their initial bug report submissions. We also find that these features significantly affect the bug‐fixing process. On the basis of our findings, we build and evaluate classification models using four different text‐classification techniques to predict key features by leveraging historical bug‐fixing knowledge. The evaluation results show that our models can effectively predict the key features. Our comparative study of different text‐classification techniques shows that naïve Bayes multinomial (NBM) outperforms other techniques. Our findings can benefit reporters to improve the contents of bug reports.