To alleviate the class imbalance problem of software defect prediction and avoid the influence of overfitting on the accuracy of the defect prediction model, this study proposes an oversampling method for software defect prediction based on heterogeneous distance ranking (HDR). First, a minority of instances are distinguished by three classes to remove noise instances and reduce overfitting caused by noise data. Then, instances are ranked based on heterogeneous distances and paired with highly similar ones to generate new instances for the improvement of new instance diversity. Valuable minority instances that were deleted are restored afterward. The experiment compares the HDR algorithm with the SMOTE and the Borderline-SMOTE algorithms, and the RF classifier is used on the eight actual project data sets of NASA. The results show that there are 7.7% and 10.6% performance improvements on the F1-measure and G-Mean indicators respectively. Experimental results show that the HDR algorithm is significantly better than other algorithms in processing software defect prediction data sets with large data volumes and high imbalance rates.