论文标题
这个错误严重吗？一个基于文本兼图的模型，用于错误严重性预测
Is this bug severe? A text-cum-graph based model for bug severity prediction
论文作者
论文摘要
大型软件系统的存储库已变得司空见惯。这种大规模的扩展导致这些软件平台中各种问题的出现，包括（i）易用错误的软件包，（ii）关键错误和（iii）错误的严重性。重要目标之一是要挖掘这些错误，并向开发人员推荐它们以解决它们。第一步是必须准确检测错误的严重程度。在本文中，我们承担了预测不久的将来错误严重程度的任务。上下文化的神经模型构建在错误的文本描述上，用户对错误的评论有助于实现合理的性能。有关错误如何相互关联的更多信息，可以以图形的形式汇总其影响包的方式，并与文本一起使用以获得其他好处。
Repositories of large software systems have become commonplace. This massive expansion has resulted in the emergence of various problems in these software platforms including identification of (i) bug-prone packages, (ii) critical bugs, and (iii) severity of bugs. One of the important goals would be to mine these bugs and recommend them to the developers to resolve them. The first step to this is that one has to accurately detect the extent of severity of the bugs. In this paper, we take up this task of predicting the severity of bugs in the near future. Contextualized neural models built on the text description of a bug and the user comments about the bug help to achieve reasonably good performance. Further information on how the bugs are related to each other in terms of the ways they affect packages can be summarised in the form of a graph and used along with the text to get additional benefits.
