Existing software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

现有的软件智能缺陷分类方法不考虑雷达特征和先验统计信息。因此，将这些方法应用于雷达软件测试和验证时，缺陷分类的准确率和召回率较差，影响了软件缺陷的复用有效性。针对这一问题，本文提出了一种基于潜在狄利克雷分配（LDA）主题模型的雷达软件智能缺陷分类方法。所提出的方法包括基于雷达域字典的缺陷文本分割算法、结合雷达软件需求的改进LDA模型以及基于改进LDA模型的雷达软件缺陷的顶级获取和分类方法。将所提出的方法应用于典型的雷达软件缺陷，以验证其有效性和适用性。应用结果表明，与其他缺陷分类方法相比，提出的方法的预测准确率和召回率提高了15~20%。因此，所提出的方法可以有效地应用于雷达软件缺陷的分割和分类，以提高雷达软件缺陷的识别充分性。