Embedded software fault prediction based on back propagation neural network

被引：3
|
作者：
Zong, Pengyang [1 ]
Wang, Yichen [1 ]
Xie, Feng [2 ]
机构：
[1] Beihang Univ, Sci & Technol Reliabil & Environm Engn Lab, Beijing, Peoples R China
[2] Antares Testing Int Ltd, Beijing, Peoples R China
来源：
2018 IEEE 18TH INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C) | 2018年
关键词：
Embedded software; software metrics; fault prediction; back propagation neural network;
D O I：
10.1109/QRS-C.2018.00098
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Predicting software faults before software testing activities can help rational distribution of time and resources. Software metrics are used for software fault prediction due to their close relationship with software faults. Thanks to the non-linear fitting ability, Neural networks are increasingly used in the prediction model. We first filter metric set of the embedded software by statistical methods to reduce the dimensions of model input. Then we build a back propagation neural network with simple structure but good performance and apply it to two practical embedded software projects. The verification results show that the model has good ability to predict software faults.
引用
收藏
页码：553 / 558
页数：6
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