Abstract: In the application of software defect prediction technology， the project to be predicted may be a brand new project， or the historical data of the project to be predicted is insufficient. One solution is to use a project （source project） with sufficient data to build a model to complete the prediction of a new project （target project）， and mainly use traditional machine learning methods to perform feature transfer learning on the source project and the target project to complete defect prediction. There is a large difference in the distribution of data between different projects， and the feature representation ability learned by traditional machine methods is weak and the defect prediction performance is poor. In response to this problem， a cross-item defect prediction method based on stacked denoising autoencoders is proposed from the perspective of deep learning. This method combines stacked denoising autoencoders and maximum mean difference distance， which can effectively extract the transferable deep-level feature representation of source items and target items， based on which an effective defect number prediction model can be trained. The experimental results show that compared with the classical cross-item defect prediction methods Burak filtering method， Peters filtering method， TCA and TCA+ on Relink dataset and AEEEM dataset， this method achieves the best prediction results in most cases.

Key words: cross-project software defect prediction, stacked denoising autoencoders, maximum mean difference distance, deep feature representation