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Cross project defect prediction, involves predicting software defects in the new software project based on the historical data of another project. Many researchers have successfully developed defect prediction models using conventional machine learning techniques and statistical techniques for within project defect prediction. Furthermore, some researchers also proposed defect prediction models for cross project defect prediction. However, it is observed that the performance of these defect prediction models degrade on different datasets. The completeness of these models are very poor. We have investigated the use of extreme learning machine (ELM) for cross project defect prediction. Further, this paper investigates the use of ELM in non linear heterogeneous ensemble for defect prediction. So, we have presented an efficient nonlinear heterogeneous extreme learning machine ensemble (NH ELM) model for cross project defect prediction to alleviate these mentioned issues. To validate this ensemble model, we have leveraged twelve PROMISE and five eclipse datasets for experimentation. From experimental results and analysis, it is observed that the presented nonlinear heterogeneous ensemble model provides better prediction accuracy as compared to other single defect prediction models. The evidences from completeness analysis also proved that the ensemble model shows improved completeness as compared to other single prediction models for both PROMISE and eclipse datasets.

Cross Project Software Defect Prediction using Extreme Learning Machine: An Ensemble based Study