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Software Defect Prediction via Transfer Learning based Neural Network

机译：基于传输学习神经网络的软件缺陷预测

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摘要

Software defect (Bug) prediction plays an important role in improving software quality. Many software defect prediction approaches have been proposed and achieved great effects in the real-world. However, the existing works are usually constrained in only one project, hence their effectiveness on cross-project defect prediction (cross-prediction) is usually poor. This is mainly because of the problem of class imbalance and feature distribution differences between the source and target projects. In this paper, we proposed an effective software defect prediction method called Transfer Component Analysis Neural Network (TCANN), by adequately considering the noise data, the class imbalance in data settings and transfer learning among cross-project. There are three parts in TCANN, aiming to solve the above mentioned three problems respectively. First, the Inter Quartile Range (IQR) based method is proposed for noise removal in datasets. Second, The transfer component analysis method is used to reduce the feature distribution differences between source and target data. Third, dynamic sampling neural network is proposed for dealing with class imbalance problem of the training dataset. Based on the classic open-source datasets collected by previous researchers, our experimental results show that TCANN improves the performance of both within-project and cross-project defect prediction in comparison with other methods.
机译：软件缺陷（错误）预测在提高软件质量方面发挥着重要作用。已经提出了许多软件缺陷预测方法，并在现实世界中实现了很大的效果。然而，现有的作品通常仅在一个项目中受到限制，因此它们对交叉项目缺陷预测（交叉预测）的有效性通常差。这主要是因为源和目标项目之间的类别不平衡和特征分布差异的问题。在本文中，我们提出了一种有效的软件缺陷预测方法，称为传输组件分析神经网络（TCann），通过充分考虑噪声数据，数据设置中的类别不平衡和交叉项目之间的传输学习。 TCANN有三个部分，旨在分别解决上述三个问题。首先，提出了基于间距的基于四分位数（IQR）的方法，用于在数据集中删除噪声。其次，转移组件分析方法用于减少源数据和目标数据之间的特征分布差异。第三，提出了处理训练数据集的类别不平衡问题的动态采样神经网络。基于以前研究人员收集的经典开源数据集，我们的实验结果表明，与其他方法相比，TCANN提高了项目内和交叉项目缺陷预测的性能。

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