Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.

中文翻译：

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java的公共统一错误数据集及其关于度量和错误预测的评估

许多研究人员已经创建并使用错误数据集来构建和验证新的错误预测模型。在这项工作中，我们的目标是收集现有的基于公共源代码度量的错误数据集并统一其内容。此外，我们希望评估收集到的大量指标以及统一错误数据集在错误预测中的能力。我们考虑了 5 个公共数据集，并为数据集中的每个系统下载了相应的源代码，并进行了源代码分析以获得一组通用的源代码指标。这样，我们也在类和文件级别生成了统一的错误数据集。我们调查了不同错误数据集的度量定义和值的转移。最后，我们使用决策树算法来展示数据集在错误预测中的能力。我们发现原始和新计算的度量值在统计上存在显着差异；此外，符号和定义可能有很大不同。我们比较了原始度量套件和扩展度量套件（项目内学习）的错误预测能力。之后，我们将所有类（和文件）合并到一个包含 47,618 个元素（文件为 43,744 个）的大型数据集，我们也评估了在这个大型数据集上构建的错误预测模型。最后，我们还研究了错误预测模型和数据集的跨项目能力。我们向所有人公开了统一数据集。通过使用公共统一数据集作为不同错误预测相关调查的输入，研究人员可以使他们的研究具有可重复性，从而能够得到验证和验证。