TY - GEN
T1 - A Software Reliability Prediction Model
T2 - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
AU - Fu, Yangzhen
AU - Zhang, Hong
AU - Zeng, Chenchen
AU - Feng, Chao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/7
Y1 - 2017/8/7
N2 - With the development of software reliability research and machine learning, many machine learning models have been used in software reliability prediction. A long short term memory network (LSTM) modeling approach for software reliability prediction is proposed. Profit from its particular data flow control structure, the model overcomes the vanishing and exploding sensitivity of simple recursive neural network for software reliability prediction. Proposed approach also combines with layer normalization and truncate back propagation. To some extent, these two methods promote the effect of the proposed model. Compared with the simple recursive neural network, numerical results show that our proposed approach has a better performance and robustness with respect to software reliability prediction.
AB - With the development of software reliability research and machine learning, many machine learning models have been used in software reliability prediction. A long short term memory network (LSTM) modeling approach for software reliability prediction is proposed. Profit from its particular data flow control structure, the model overcomes the vanishing and exploding sensitivity of simple recursive neural network for software reliability prediction. Proposed approach also combines with layer normalization and truncate back propagation. To some extent, these two methods promote the effect of the proposed model. Compared with the simple recursive neural network, numerical results show that our proposed approach has a better performance and robustness with respect to software reliability prediction.
KW - long short term memory network
KW - software reliability prediction
KW - vanishing and exploding sensitivity
UR - https://www.scopus.com/pages/publications/85034416029
U2 - 10.1109/QRS-C.2017.115
DO - 10.1109/QRS-C.2017.115
M3 - 会议稿件
AN - SCOPUS:85034416029
T3 - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
SP - 614
EP - 615
BT - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 July 2017 through 29 July 2017
ER -