Visible to the public Embedded Software Fault Prediction Based on Back Propagation Neural Network

TitleEmbedded Software Fault Prediction Based on Back Propagation Neural Network
Publication TypeConference Paper
Year of Publication2018
AuthorsZong, P., Wang, Y., Xie, F.
Conference Name2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)
Date PublishedJuly 2018
ISBN Number978-1-5386-7839-8
Keywordsback propagation neural network, Backpropagation, Biological neural networks, Correlation, Embedded Software, embedded software fault prediction, Embedded systems, fault prediction, filter metric set, formal verification, Measurement, Metrics, metrics testing, neural nets, Neurons, nonlinear fitting ability, program testing, pubcrawl, Software, software fault tolerance, software metrics, software testing activities, statistical analysis, Training

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.

Citation Keyzong_embedded_2018