Software defect prediction is one of the software engineering's most active research fields. Most of the existing work focuses on Homogeneous Cross Project Defect Prediction (CPDP), in which the model is trained by the use of a common metric set extracted from the source and target project. Our article emphasizes the heterogeneous CPDP modeling (HCPDP) that develops a defect prediction model based on a metric choice and metric matching strategy and also shows a comparable value distribution for a specified couple of datasets. It assesses experimentally and theoretically HCPDP modeling whose three primary elements include feature ranking and feature selection, metric matching an binary classification of unlabeled target instances. Results indicate that feature selection techniques have a very tiny effect on the performance of defect prediction, and the Gradient Boosting classification system provides the highest findings when used in combination with the Pearson Correlation technique compared to other classifiers used.