Dimensional Reduction on Cross Project Defect Prediction
Aries Saifudin, Yulianti Yulianti
Abstract
Abstract The complexity of the software can increase the possibility of defects. Defective software can cause high losses. The software containing defects can cause large losses. Most software developers don’t document their work properly so that making it difficult to analyse software development history data. The cross-project software defect prediction used several datasets from different projects and combining for training and testing. The dataset with high dimension can cause bias, contain irrelevance data, and require large resources to process it. In this study, several dimensional reduction algorithm and Decision Tree as classifier. Based on the analysis using ANOVA, all models that implement dimensional reduction can significantly improve the performance of the Decision Tree model.