The traditional software defect prediction methods have weak adaptive ability for cross-project defect prediction

largely because of feature distribution differences between the source and target projects.In order to resolve this problem

we propose a novel weighted naive Bayes transfer learning algorithm.Firstly

the feature information of the test data and training data are collected;next

our solution computes feature differences

and transfers cross-project data differences into the weights of the training data;finally

on these weighted data

the defect prediction model is built.Our experiments are conducted on eight open-source projects

and experimental results demonstrate that our method significantly improves cross-project defect prediction performance

compared to other methods.