摘要:在所有的软件系统开发过程中，Bug的存在是不可避免的问题.对于软件系统的开发者来说，修复Bug最有利的工具就是Bug报告.但是人工识别Bug报告会给开发人员带来新的负担，因此，自动对Bug报告进行分类是一项很有必要的工作.基于此，提出用基于极速学习机的方法来对Bug报告进行分类.具体而言，主要解决Bug报告自动分类的3个问题：第1个是Bug报告数据集里不同类别的样本数量不平衡问题；第2个是Bug报告数据集里被标注的样本不充足问题；第3个是Bug报告数据集总体样本量不充足问题.为了解决这3个问题，分别引入了基于代价的有监督分类方法、基于模糊度的半监督学习方法以及样本迁移方法.通过在多个Bug报告数据集上进行实验，验证了这些方法的可行性和有效性.



Abstract:Bug is an unavoidable problem in the development of all software systems. For developers of software system, bug report is a powerful tool for fixing bugs. However, manual recognition on bug reports tends to be time-consuming and not economical. It thus becomes significant to advance the automated classification approach to provide clear guidelines on how to assign a reasonable severity to a reported bug. In this study, several algrithoms are proposed based on extreme learning machine to automatically classify bug reports. Concretely, this study focuses on three problems in the field of bug report classification. The first one is the imbalanced class distribution in bug report dataset; the second is the insufficient labeled sample in bug report dataset; the last is the limited training data available. In order to solve these issues, three methods are proposed based on cost-sensitive supervised classification, semi-supervised learning, and sample transferring, respectively. Extensive experiments on real bug report datasets are conducted, and the results demonstrate the practicability and effectiveness of the proposed methods.



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