基于改进的非参数方法的软件失效预测模型

Software Failure Prediction Model Based on Improved Nonparametric Method

基于主成分分析(PCA)和改进的N-W非参数估计法(INW)提出了一种新的软件失效预测模型。首先,通过对非参数估计的训练样本集进行主成分分析来减少非参数回归估计和预测的输入因子数,再利用PCA计算的方差贡献率作为非参数方法中带宽矩阵的权重,消除各输入因子对结果的作用程度不同所造成的影响,进而建立软件失效预测模型。最后基于一组真实软件失效数据集Eclipse JDT进行实例分析。结果表明,基于改进的非参数方法的软件失效预测模型在预测的精度和稳定性上得到了进一步提高。在后10步的预测范围内,预测值的平均误差为16.2575,均方百分比误差为0.0726。

Based on principal component analysis （PCA） and improved N-W nonparametric estimation method （INW）, a new software failure prediction model was presented. First of all, through the principal component analysis of training sample set of nonparametric estimation, the input number of nonparametric method was reduced. Then the variance contribution ratio of PCA was used as the weight of the bandwidth matrix in nonparametric estimation method, the impact of each imput factor on the results was eliminated in a different extent and software failure prediction models were built. Finally, this paper gave example analysis based on one real software failure data set Eclipse JDT. The results show that the failure prediction model based on improved nonparametric method has made further improvement in prediction precision and stability. Within the forecast range of the last ten steps, the average error of predictive value is 16. 2575, and the mean square error is 0. 0726.