In order to improve the efficiency and quality of aerospace software testing, an approach to cross-company aerospace software defect prediction was proposed, especially for the scarcity of within-company software and the long cycle of development. Considering the complexity, large scale, and independent functions of aerospace software, the idea of building a defect prediction model based on static classification was proposed. In this paper, the transfer learning method was introduced. Using the nearest neighbor classifier and data gravity model, the distribution characteristics of training data were corrected to improve the similarity between training data and target data. In order to improve the generalization ability of the model to adapt to the diversity of target data, a small amount of target data was added to the training data for model training. The approach was applied to the test for aerospace software testing. The results of application show that, compared with existing software defect prediction methods, the proposed method can effectively improve the recall rate (close to 0.6) with a low false alarm rate (not higher than 0.3). The overall credibility is effectively enhanced (G-measure is over 0.6), and the method has high stability and strong generalization ability. This method can control the test scale in practical projects and improve testing efficiency.