Software defect prediction is a typical unbalanced learning problem. We propose a CCS-SVM software defect prediction model based on cost sensitive SVM algorithm improved by the CSSVM and clustering algorithm. In the CCSSVM prediction model, we combine SVM and the cost of class misclassification, take unbalanced data evaluation index as the objective function, and optimize the misclassification cost factor so as to enhance the recognition rate of the minority class samples. We find the center point of each sample through clustering, define the class confidence for each sample according to the distance of the sample to its center point, assign different misclassification cost factors to different samples, and introduce the class confidence of each sample to the optimization problem of cost sensitive SVM, and improve the robustness of the algorithm and classification performance of SVM. To enhance the generalization ability of the model, we use the genetic algorithm to optimize feature selection and model parameters. Experimental results of the NASA Metric Data Program (MDP) dataset show that our method is  significantly improved in the Gmean and Fmeasure value for model evaluation.