Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non-Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class-imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IM_NRFixer, a novel and hybrid NRF prediction tool. IM_NRFixer uses three different techniques to combat class-imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IM_NRFixer models on four large and open-source projects of Bugzilla repository. Our results show that IM_NRFixer outperforms conventional machine learning techniques. IM_NRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively.

中文翻译：

---

IMNRFixer：一种缓解类不平衡问题的混合方法，用于预测不可重现错误的可修复性

软件维护是软件开发生命周期中的一个重要阶段。软件项目维护错误存储库以收集、组织和跟踪错误报告。这些错误报告由众多软件开发人员解决。每当指定的开发人员未解决报告的错误时，他会将错误报告的解决方法标记为不可重现 (NR)。当重新考虑 NR 错误时，很少有问题得到解决，并且它们的解决方案从 NR 更改为修复 (NRF)。本文的主要目的是预测这些可修复的 NRF 错误报告。从 NR 错误预测 NRF 错误的一个主要挑战是只有一小部分 NR 错误得到修复，即类不平衡问题。例如，NRF 错误在 NetBeans、Eclipse、Open Office 和 Mozilla Firefox 项目中分别仅占 8.64%、4.73%、4.56% 和 1.06%。在本文中，我们致力于提高这些不平衡数据集的分类性能。我们建议 IM_NRFixer，一种新颖的混合 NRF 预测工具。IM _NRFixer使用三种不同的技术来解决类不平衡问题：欠采样、过采样和集成模型。_{我们评估了 IM NRFixer}模型在 Bugzilla 存储库的四个大型开源项目上的性能。我们的结果表明 IM _NRFixer优于传统的机器学习技术。IM _NRFixer在预测 NetBeans、Eclipse、Open Office 和 Mozilla Firefox 项目的少数类 (NRF) 时分别实现了高达 71.7%、93.1%、91.7% 和 96.5% 的性能。