| 软件模块故障倾向预测方法研究 |
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| 引用本文： | 罗云锋,普杰,贲可荣.软件模块故障倾向预测方法研究[J].武汉大学学报(信息科学版),2010(5). |
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| 作者姓名： | 罗云锋 普杰 贲可荣 |
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| 作者单位： | 海军工程大学计算机工程系; |
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| 基金项目： | 国防预研基金资助项目(513270104) |
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| 摘 要： | 研究了在区分故障严重程度下的软件模块故障倾向预测方法,将故障分为高严重程度和低严重程度两种类型,用统计分析和机器学习方法分析静态代码度量与故障倾向之间的关系。以公开和私有两种类型的失效数据集作为实验数据,分析发现,故障的严重程度影响预测性能,预测不同严重程度的故障需要选择不同的度量和分类模型,预测低严重程度故障的性能好于预测高严重程度故障的性能。
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| 关 键 词： | 软件模块故障倾向 故障严重程度 统计分析 机器学习 |
Fault-proneness Prediction of Software Modules |
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| LUO Yunfeng PU Jie BEN Kerong.Fault-proneness Prediction of Software Modules[J].Geomatics and Information Science of Wuhan University,2010(5). |
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| Authors: | LUO Yunfeng PU Jie BEN Kerong |
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| Institution: | LUO Yunfeng1 PU Jie1 BEN Kerong1(1 Department of Computer Engineering,Navy University of Engineering,717 Jiefang Avenue,Wuhan 430033,China) |
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| Abstract: | Utilizing a public dataset and a private dataset,we employed the statistics analysis and machine learning methods to empirically investigate the relation between static code metrics and the proneness of software modules.We conclude that fault severity impacts the performance of fault-proneness prediction,and we should take the fault severity into account when choosing appropriate metrics and classification models.We also conclude that the performance of prediction for low severity faults is better than the ... |
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| Keywords: | fault proneness of software modules fault severity statistics analysis machine learning |
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