Parallel Defect Detection Model on Uncertain Data for GPUs Computing by a Novel Ensemble Learning

To detect defect is an important concept in machine leaning techniques and ambiguous dataset which develops into a challenging issue, as the software product expands in terms of size and its complexity. This chapter reveals an applied novel multi-learner model which is ensembled to predict software metrics using classification algorithms and propose algorithm applied in parallel method for detection on ambiguous data using density sampling and develop an implementation running on both GPUs and multi-core CPUs. The defect on the NASA PROMISE defect dataset is adequately predicted and classified using these models and implementing GPU computing. The performance compared to the traditional learning models improved algorithm and parallel implementation on GPUs shows less processing time in ensemble model compared to decision tree algorithm and effectively optimizes the true positive rate.