The prediction of bug types provides useful insights into the software maintenance process. It can improve the efficiency of software testing and help developers adopt corresponding strategies to fix bugs before releasing software projects. Typically, the prediction tasks are performed through machine learning classifiers, which rely heavily on labeled data. However, for a software project that has insufficient labeled data, it is difficult to train the classification model for predicting bug types. Although labeled data of other projects can be used as training data, the results of the cross-project prediction are often poor. To solve this problem, this paper proposes a cross-project bug type prediction framework based on transfer learning. Transfer learning breaks the assumption of traditional machine learning methods that the training set and the test set should follow the same distribution. Our experiments show that the results of cross-project bug type prediction have significant improvement by adopting transfer learning. In addition, we have studied the factors that influence the prediction results, including different pairs of source and target projects, and the number of bug reports in the source project.

中文翻译：

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基于迁移学习的跨项目bug类型预测

错误类型的预测为软件维护过程提供了有用的见解。它可以提高软件测试的效率，并帮助开发人员在发布软件项目之前采用相应的策略来修复错误。通常，预测任务是通过机器学习分类器执行的，这些分类器严重依赖于标记数据。但是，对于标记数据不足的软件项目，很难训练用于预测错误类型的分类模型。虽然其他项目的标注数据可以作为训练数据，但是跨项目预测的结果往往很差。针对这一问题，本文提出了一种基于迁移学习的跨项目bug类型预测框架。迁移学习打破了传统机器学习方法的假设，即训练集和测试集应该遵循相同的分布。我们的实验表明，通过采用迁移学习，跨项目错误类型预测的结果有显着改善。此外，我们还研究了影响预测结果的因素，包括源项目和目标项目的不同对，以及源项目中的错误报告数量。