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Multi-Feature Approach for Bug Severity Assignment

机译：错误严重性分配的多特征方法

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摘要

When bug reports are submitted through bug tracking systems, they are analysed manually to identify their severity levels. A severity level specifies the negative impact of a bug on a system. With the huge number of submitted reports, setting the severity class manually is tedious and time consuming. Moreover, some bug types are reported more often than other types, which leads to imbalanced bug repositories. This paper proposes a multi-feature approach for automatic severity assignment, which leverages lexical, semantic, and categorical properties of the bug reports. The proposed approach utilizes word embeddings, topic model, vector space model, and an adapted K-Nearest Neighbour technique. Moreover, the impact of utilizing two sampling techniques, namely SMOTE and cluster-based under-sampling (CBU), were investigated. Experiments over two open source repositories, Eclipse and Mozilla, demonstrated that the proposed approach is superior to two previous studies.
机译：通过错误跟踪系统提交错误报告时，将手动分析它们以识别其严重性级别。严重性级别指定系统上的错误的负面影响。通过大量提交的报告，手动设置严重性类是繁琐且耗时的。此外，一些错误类型的频率比其他类型更频繁地报告，这导致错误的错误存储库。本文提出了一种自动严重性分配的多特征方法，它利用了错误报告的词汇，语义和分类属性。所提出的方法利用Word Embeddings，主题模型，矢量空间模型和适应的k最近邻技术。此外，研究了利用两种采样技术的影响，即Smote和基于簇的底下取样（CBU）。两次开源储存库，日食和Mozilla的实验表明，所提出的方法优于前两项研究。

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