Combing data filter and data sampling for cross- Company defect prediction: An empricial study

Abstract

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, the CC data has the highly imbalanced nature between the defectiveprone and non-defective classes, which will degrade the performance of CCDP. To address such issues, this paper proposes an approach, in which data sampling is combined with data filter, to overcome these problems. Data sampling seeks a more balanced dataset through the addition or removal of instances, while data filter is a process of filtering out the irrelevant CC data so that the performance of CCDP models can be improved. We employ two data filtering methods called NN filter and DBSCAN filter combined with SMOTE (Synthetic Minority Oversampling Technique) and RUS (Random Under- Sampling). Eight different approaches would be produced when combing these four techniques: 1- NN filter performed prior to RUS; 2- NN filter performed after RUS; 3- NN filter performed prior to SMOTE; 4- NN filter performed after SMOTE; 5- DBSCAN filter performed prior to RUS; 6- DBSCAN filter performed after RUS; 7- DBSCAN filter performed prior to SMOTE; 8- DBSCAN filter performed after SMOTE. The empirical study was carried out on 15 publicly available project datasets. The experimental results demonstrate that NN filter performed prior to RUS (Approach 1) performs better than the other seven approaches.