Abstract: In recent years,as software requirements increase,developers have introduced a large amount of clone code into the project by reusing existing code.As the software version is updated,the clone code changes and it may become a burden on software maintenance.Researchers have attempted to use the machine learning to conduct research on the prediction of clone code consistency,and help the software quality assurance team to allocate maintenance resources more effectively by predicting whether changes to cloned code will cause additional maintenance costs,thereby improving work efficiency and reducing maintenance costs.However,in the early stage of software development,software projects are often not fully evolved,and historical data is lacking for constructing an effective predictive model.Therefore,cross-project clone code consistency prediction methods are proposed.In this paper,we propose a cross-project clone code consistency prediction method via transfer learning and oversampling technology (CPCCP+).This method aims to match test set and training set into kernel space,reduce the distribution discrepancy of cross-project data by transfer component analysis,and alleviate the class imbalance issue to improve the performance of cross-project prediction model.In terms of experimental datasets,this paper selects seven open source datasets,which can form 42 combinations of cross-project clone code consistency prediction tasks totally.In terms of model performance comparison,the CPCCP+ proposed in this paper is compared with the method only using base classifier.The evaluation metrics include precision,recall and F-measure.The experimental results show that CPCCP+ can more effectively perform cross-project clone code consistency prediction.

Key words: Code clone, Consistent change, Cross-project prediction, Oversampling technology, Transfer learning