Abstract: According to the metrics information and defects found in a software product, we can use software defect prediction technology to predict more defects that may also exist as early as possible, then testing and validation resources are allocated based on the prediction result appropriately. Defect prediction based on machine learning techniques can find software defects comprehensively and automatically, and it is becoming one of the main methods of current defect prediction technologies. In order to improve the efficiency and accuracy of prediction, selection and research of machine learning algorithms is the critical part. In this paper, we do comparative analysis to different machine learning defect prediction methods, and find that different algorithms have both advantages and disadvantages in different evaluation indexes. Taking these advantages, we refer to the stacking integration learning method and present a combined software defect prediction model. In this model, we first predict once, then add the prediction results of different methods in the original dataset as new software metrics, and then predict again. Finally, we make experiments on Eclipse dataset. Experimental results show that this model is technical feasibility, and can decrease the cost of time and improve the accuracy.