Is the Number of Faults Helpful for Cross-Company Defect Prediction?

Abstract

Abstract-In the field of Cross-company Defect Prediction (CCDP), how to deal with the data to make it more accurately predict the cross-company software defects is the focus problem we need to consider. Now a mainstream idea is to determine the weight of the training data based on the similarity between the training data and the test set, and then build the model on the basis of these weighted data. However, sometimes, when we deal with some problems with imbalance class, directly using the weight calculated above may lead to errors. Because a large number of non-defective instance's weight accumulation will lead to the defective instance's weight has a little impact on the final result. This is why we need to consider the addition of the number of defects. Considering the number of defects will effectively eliminate the impact of a large number of non-defective instance's weight accumulation. Therefore, we propose a Transfer-learning Naï ve Bayes model considering the number of defective information(NTNB). The method consists of two major stages: weight the data and build the prediction model. In the stage of weighting the data, we not only consider the similarity between the data but also consider the number of defects to get the final weights for data. And we conducted a set of comparative experiments on six open crosscompany datasets. The results show that considering the number of defects information can effectively avoid some defective instance is misjudged as non-defective instance, and improve the accuracy of prediction in some unbalanced problems.