TY - GEN
T1 - A New Software Reliability Model for Open Stochastic System Based on NHPP
AU - Li, Qiuying
AU - Zhang, Chao
AU - Zhang, Hong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/7
Y1 - 2017/8/7
N2 - Among software reliability growth models (SRGMs), the NHPP models perform well in practice. However, the traditional NHPP models still have many problems which are mainly due to the following unreasonable assumptions: 1) fault detection rate subjects to constant or regular change, 2) the testing environment and the final field environment are the same, 3) the testing environment is a completely closed system that does not interact with the outside world. Obviously, these assumptions are not consistent with the reality. Based on the above considerations, we take into account the random interference of the testing system, the uncertainty of the field environment and the openness of the testing system to establish a reliability model which describes the open stochastic system (OSS). Finally, the experimental results show that the proposed model has excellent ability of fitting and predicting.
AB - Among software reliability growth models (SRGMs), the NHPP models perform well in practice. However, the traditional NHPP models still have many problems which are mainly due to the following unreasonable assumptions: 1) fault detection rate subjects to constant or regular change, 2) the testing environment and the final field environment are the same, 3) the testing environment is a completely closed system that does not interact with the outside world. Obviously, these assumptions are not consistent with the reality. Based on the above considerations, we take into account the random interference of the testing system, the uncertainty of the field environment and the openness of the testing system to establish a reliability model which describes the open stochastic system (OSS). Finally, the experimental results show that the proposed model has excellent ability of fitting and predicting.
KW - NHPP
KW - fault detection rate
KW - field environment
KW - random factors
KW - software reliability growth models
UR - https://www.scopus.com/pages/publications/85034418726
U2 - 10.1109/QRS-C.2017.120
DO - 10.1109/QRS-C.2017.120
M3 - 会议稿件
AN - SCOPUS:85034418726
T3 - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
SP - 624
EP - 625
BT - Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2017
Y2 - 25 July 2017 through 29 July 2017
ER -