基于PSO-BP的软件缺陷预测模型

软件缺陷检测旨在自动检测程序模块中是否包含缺陷，从而加速软件测试过程，提高软件系统的质量。针对传统软件缺陷预测模型被限制在一定的应用范围而影响其预测的准确性和适用性，提出了一种基于PSO-BP软件缺陷预测模型。该模型运用粒子群优化算法优化BP神经网络的权值和阈值，采用交叉验证的方式进行实验，并与传统的机器学习方法J48和BP神经网络等方法进行了比较。实验结果表明提出的方法具有较高的预测准确性。

Software defect prediction model based on PSO-BP

Software defect detection aims to automatically identify defective software modules that accelerates efficiently software test and improves the quality of a software system. Due to the application of traditional software prediction model being limited for its low accuracy and applicability, this paper puts forward a software prediction model based on PSO-BP, which employs Particle Swarm Optimization（PSO） to optimize weight and threshold value of BP. It uses cross-validation method as experiment method, and compares the results with other machine learning methods-BP and J48. The results show that PSO-BP has higher prediction accuracy.