A software defect report is a bug in the software system that developers and users submit to the software defect library during software development and maintenance. Managing a software defect report that is overwhelming is a challenging task. The traditional method is manual identification, which is time-consuming and laborious and delays the repair of important software defects. Based on the above background, the purpose of this paper is to study the software defect prediction (SDP) model based on LASSO–SVM. In this paper, the problem of poor prediction accuracy of most SDP models is proposed. A SDP model combining minimum absolute value compression and selection method and support vector machine algorithm is proposed. Firstly, the feature selection ability of the minimum absolute value compression and selection method is used to reduce the dimension of the original data set, and the data set not related to SDP is removed. Then, the optimal value of SVM is obtained by using the parameter optimization ability of cross-validation algorithm. Finally, the SDP is completed by the nonlinear computing ability of SVM. The accuracy of simulation results is 93.25% and 66.67%, recall rate is 78.04%, and f-metric is 72.72%. The results show that the proposed defect prediction model has higher prediction accuracy than the traditional defect prediction model, and the prediction speed is faster.

软件缺陷报告是软件系统中的错误，开发人员和用户在软件开发和维护期间会将其提交到软件缺陷库。管理大量的软件缺陷报告是一项艰巨的任务。传统的方法是手动识别，这既费时又费力，并且延迟了重要软件缺陷的修复。基于上述背景，本文的目的是研究基于LASSO-SVM的软件缺陷预测（SDP）模型。本文提出了大多数SDP模型的预测精度较差的问题。提出了结合最小绝对值压缩和选择方法以及支持向量机算法的SDP模型。首先，利用最小绝对值压缩和选择方法的特征选择能力来减小原始数据集的维数，并删除与SDP不相关的数据集。然后，利用交叉验证算法的参数优化能力获得支持向量机的最优值。最后，通过SVM的非线性计算能力来完成SDP。仿真结果的准确度分别为93.25％和66.67％，召回率为78.04％，f-metric为72.72％。结果表明，提出的缺陷预测模型比传统的缺陷预测模型具有更高的预测精度，并且预测速度更快。利用交叉验证算法的参数优化能力获得支持向量机的最优值。最后，通过SVM的非线性计算能力来完成SDP。仿真结果的准确度分别为93.25％和66.67％，召回率为78.04％，f-metric为72.72％。结果表明，所提出的缺陷预测模型比传统的缺陷预测模型具有更高的预测精度，并且预测速度更快。利用交叉验证算法的参数优化能力获得支持向量机的最优值。最后，通过SVM的非线性计算能力来完成SDP。仿真结果的准确度分别为93.25％和66.67％，召回率为78.04％，f-metric为72.72％。结果表明，提出的缺陷预测模型比传统的缺陷预测模型具有更高的预测精度，并且预测速度更快。