论文标题
基于深度学习模型的软件缺陷预测：绩效研究
Software Defect Prediction Based On Deep Learning Models: Performance Study
论文作者
论文摘要
近年来，缺陷预测是主要软件工程问题之一，它一直是研究人员的重点，因为它在估计软件错误和错误模块中具有关键作用。提高预测准确性的研究人员为软件缺陷预测开发了许多模型。但是，为了获得更好的结果，有许多关键条件和理论问题。在本文中，部署了两个深度学习模型，即堆栈稀疏自动编码器（SSAE）和深信念网络（DBN），以对NASA数据集进行分类，而NASA数据集则是不平衡且示例不足的。根据进行的实验，具有足够样品的数据集的准确性得到了增强，并且在大多数评估指标中，与DBN模型相比，SSAE模型在此旁边获得了更好的结果。
In recent years, defect prediction, one of the major software engineering problems, has been in the focus of researchers since it has a pivotal role in estimating software errors and faulty modules. Researchers with the goal of improving prediction accuracy have developed many models for software defect prediction. However, there are a number of critical conditions and theoretical problems in order to achieve better results. In this paper, two deep learning models, Stack Sparse Auto-Encoder (SSAE) and Deep Belief Network (DBN), are deployed to classify NASA datasets, which are unbalanced and have insufficient samples. According to the conducted experiment, the accuracy for the datasets with sufficient samples is enhanced and beside this SSAE model gains better results in comparison to DBN model in the majority of evaluation metrics.
