Automatic Issue Classifier：一个用于问题报告分类的迁移学习框架
Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports
摘要：
问题跟踪系统在软件行业中用于促进维护活动，使软件保持健壮并与不断变化的行业需求保持同步。通常，用户报告的问题可以分为不同的标签，如错误报告，增强请求和与软件相关的问题。大多数问题跟踪系统使这些问题报告的标签对于问题子目录是可选的，这导致大量未标记的问题报告。在本文中，我们提出了一个国家的最先进的方法来分类的问题报告到各自的类别，即错误，增强和问题。这是一项具有挑战性的任务，因为问题报告中普遍使用非正式语言。现有的研究使用传统的自然语言处理方法，采用基于关键词的特征，这不能纳入词之间的上下文关系，因此导致高误报率和误报率。此外，以前的作品利用单标签的方法来分类的问题报告，但在现实中，一个问题的主题可以标记一个问题报告与多个标签的时间。本文介绍了我们的方法来分类的问题报告在多标签设置。我们使用一个现成的神经网络，称为RoberTa和微调它分类的问题报告。我们在GitHub上的许多工业项目的问题报告中验证了我们的方法。对于bug报告、增强和问题，我们分别获得了81%、74%和80%的F-1分数。我们还开发了一个名为自动问题分类器（AIC）的行业工具，它可以自动为GitHub存储库上新报告的问题分配高准确性的标签。
英文摘要：
Issue tracking systems are used in the software industry for the facilitation of maintenance activities that keep the software robust and up to date with ever-changing industry requirements. Usually, users report issues that can be categorized into different labels such as bug reports, enhancement requests, and questions related to the software. Most of the issue tracking systems make the labelling of these issue reports optional for the issue submitter, which leads to a large number of unlabeled issue reports. In this paper, we present a state-of-the-art method to classify the issue reports into their respective categories i.e. bug, enhancement, and question. This is a challenging task because of the common use of informal language in the issue reports. Existing studies use traditional natural language processing approaches adopting key-word based features, which fail to incorporate the contextual relationship between words and therefore result in a high rate of false positives and false negatives. Moreover, previous works utilize a uni-label approach to classify the issue reports however, in reality, an issue-submitter can tag one issue report with more than one label at a time. This paper presents our approach to classify the issue reports in a multi-label setting. We use an off-the-shelf neural network called RoBERTa and fine-tune it to classify the issue reports. We validate our approach on issue reports belonging to numerous industrial projects from GitHub. We were able to achieve promising F-1 scores of 81%, 74%, and 80% for bug reports, enhancements, and questions, respectively. We also develop an industry tool called Automatic Issue Classifier (AIC), which automatically assigns labels to newly reported issues on GitHub repositories with high accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
