基于堆叠降噪稀疏自动编码器的软件缺陷预测

特征提取是软件缺陷预测中的关键步骤，特征提取的质量决定了缺陷预测模型的性能，但传统的特征提取方法难以提取出软件缺陷数据的深层本质特征。深度学习理论中的自动编码器能够从原始数据中自动学习特征，并获得其特征表示，同时为了增强自动编码器的鲁棒性，本文提出一种基于堆叠降噪稀疏自动编码器的特征提取方法，通过设置不同的隐藏层数、稀疏性约束和加噪方式，可以直接高效地从软件缺陷数据中提取出分类预测所需的各层次特征表示。利用Eclipse缺陷数据集的实验结果表明，该方法较传统特征提取方法具有更好的性能。

Software Defect Prediction Based on Stacked Denoising Sparse Auto-encoder

Feature extraction is a key step in software defect prediction. The quality of the extracted features determines the performance of defect prediction. However, it is difficult for traditional feature extraction method to extract the deep nature features of software defect data. The auto-encoder model in the deep learning theory can automatically learn the features from original data and obtain its feature representation. Moreover, in order to enhance robustness of auto-encoder, a feature extraction method based on stacked denoising sparse auto-encoder is proposed. By setting different hidden layers, sparse parameter and noise increment methods, the required feature representation of classification and prediction is extracted directly and efficiently from software defect data. Experiment results using Eclipse defect dataset show that the proposed method has better prediction performance than traditional feature extraction method.