Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just-in-time defect prediction. Therefore, the authors propose a novel just-in-time defect prediction model named DAECNN-JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation-learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN-JDP model, they conduct extensive within-project and cross-project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN-JDP on five evaluation metrics.

中文翻译：

---

基于去噪自动编码器和卷积神经网络的项目内和跨项目实时缺陷预测

即时缺陷预测是软件缺陷预测中重要且有用的分支。目前，深度学习是人工智能领域的研究热点，可以将基本的缺陷特征组合为深度的语义特征，弥补机器学习算法的不足。但是，主流的深度学习技术尚未在即时缺陷预测中应用。因此，作者在这项研究中提出了一种基于去噪自动编码器和卷积神经网络的新型实时缺陷预测模型DAECNN-JDP，它具有三个主要优点：（i）每个维特征的位置向量的权重不同设置，可以通过自适应可训练向量自动进行训练。（ii）通过对降噪自动编码器的培训，可以获得不受噪声污染的输入特征，从而学习更鲁棒的特征表示。（iii）作者利用强大的表示学习技术卷积神经网络将基本的变化特征构造为抽象的深层语义特征。为了评估DAECNN-JDP模型的性能，他们在六个大型开源项目上进行了广泛的项目内和跨项目缺陷预测实验。实验结果表明，DAECNN-JDP在五个评估指标上具有优越性。为了评估DAECNN-JDP模型的性能，他们在六个大型开源项目上进行了广泛的项目内和跨项目缺陷预测实验。实验结果表明，DAECNN-JDP在五个评估指标上具有优越性。为了评估DAECNN-JDP模型的性能，他们在六个大型开源项目上进行了广泛的项目内和跨项目缺陷预测实验。实验结果表明，DAECNN-JDP在五个评估指标上具有优越性。