With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs’ descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words’ nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words’ semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.

中文翻译：

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通过加权图卷积网络有效预测错误修复优先级

随着软件错误数量的增加，错误修复在软件开发和维护中扮演着重要的角色。为了提高错误解决的效率，开发人员利用错误报告来解决给定的错误。特别是，bug 分类器通常依赖于 bug 的描述来建议报告的 bug 的优先级。然而，手动优先级分配是一项耗时且繁琐的任务。为了解决这个问题，最近的研究提出了许多方法来自动预测报告错误的优先级。不幸的是，这些方法仍然面临两个挑战，包括错误报告中单词的非连续语义和不平衡的数据。在本文中，我们提出了一种新方法，即基于加权损失函数的图卷积网络 (GCN) 来执行错误报告的优先级预测。对于第一个挑战，我们为错误报告构建了一个异构文本图，并应用 GCN 来提取错误报告中的单词语义。对于第二个挑战，我们在训练阶段构建了一个加权损失函数。我们对四个开源项目进行了优先级预测，包括 Mozilla、Eclipse、Netbeans 和 GNU 编译器集合。实验结果表明，我们的方法在 F 度量方面优于两种基线方法，加权平均值为 13.22%。