Transfer learning for software defect prediction

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. © 2016, Chinese Institute of Electronics. All right reserved.