Ensemble Based-Cross Project Defect Prediction

Abstract

AbstractIn Software Testing, there are typically two ways to predict defects in the software—within-project defect prediction (WPDP) and cross project defect prediction (CPDP). In this research, we are using a hybrid model for cross project defect prediction. It is a two-phase model consisting of ensemble learning (EL) and genetic algorithm (GA) phase. For our research, we used datasets from the PROMISE repository and created clusters after normalization using k-means clustering algorithm. This further helped us improve the accuracy of the model. Our dataset consists of 22 attributes and were labeled defective or not. Our results show that our hybrid model after implementing k-means clustering achieved an F1 score of 0.666. CPDP is a newer and faster approach for software defect prediction but is often error prone. This method can change the software industry as it will lead to improved software development and faster software delivery.KeywordsCross project defect predictionk-means clusteringEnsemble learningGenetic algorithmCompositional modelEnsemble model