ORESP:基于有序回归的软件缺陷严重程度预测方法  

ORESP:software defect severity prediction based on ordinal regression

作　　者：贾焱鑫 陈翔[1,2] 葛骅 杨光 林浩 Jia Yanxin;Chen Xiang;Ge Hua;Yang Guang;Lin Hao(School of Information Science&Technology,Nantong University,Nantong Jiangsu 226019,China;State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China)

机构地区：[1]南通大学信息科学技术学院,江苏南通226019 [2]南京大学计算机软件新技术国家重点实验室,南京210023

出　　处：《计算机应用研究》2021年第6期1815-1818,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(61702041);南京大学计算机软件新技术国家重点实验室开放课题(KFKT2019B14)。

摘　　要：为提高软件缺陷严重程度的预测性能,通过充分考虑软件缺陷严重程度标签间的次序性,提出一种基于有序回归的软件缺陷严重程度预测方法ORESP。该方法首先使用基于Spearman的特征选择方法来识别并移除数据集内的冗余特征,随后使用基于比例优势模型的神经网络来构建预测模型。通过与五种经典分类方法的比较,所提的ORESP方法在四种不同类型的度量下均可取得更高的预测性能,其中基于平均0-1误差(MZE)评测指标,预测模型性能最大可提升10.3%;基于平均绝对误差(MAE)评测指标,预测模型性能最大可提升12.3%。除此之外,发现使用基于Spearman的特征选择方法可以有效提升ORESP方法的预测性能。To improve the prediction performance of the severity of software defects,considering the order between different labels,this paper proposed a defect severity prediction method ORESP based on ordinal regression.This method used the Spearman-based feature selection method to identify and remove redundant features in the data set and then used a neural network based on the proportional odds model to build prediction models.By comparing with the five classical classification methods,the proposed method ORESP could achieve better prediction performance under four different categories of module metrics.In terms of MZE(average zero-one error)measure,the performance of the proposed model can be improved by 10.3%at most;in terms of MAE(mean absolute error),the performance of the proposed model can be improved by 12.3%at most.In addition,using the feature selection method based on Spearman can effectively improve the prediction performance of ORESP.

关 键 词：软件质量保障 缺陷严重程度预测 有序回归 特征选择 分类 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]