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Author:

Peng, Rui (Peng, Rui.) (Scholars:彭锐) | Ma, Xiaoyang (Ma, Xiaoyang.) | Zhai, Qingqing (Zhai, Qingqing.) | Gao, Kaiye (Gao, Kaiye.)

Indexed by:

EI Scopus

Abstract:

As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model (SRGM). The application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson process (NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent faults only become detectable after the related leading faults are detected. The second-step dependent faults can only be detected after the related first-step dependent faults are detected. Then, the combined model is built on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the proposed model. © 2019, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.

Keyword:

Poisson distribution Fault detection Software reliability Software quality Application programs

Author Community:

  • [ 1 ] [Peng, Rui]School of Economics and Management, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Ma, Xiaoyang]School of Information Management, Beijing Information Science and Technology University, Beijing; 100192, China
  • [ 3 ] [Zhai, Qingqing]School of Management, Shanghai University, Shanghai; 200444, China
  • [ 4 ] [Gao, Kaiye]School of Economics and Management, Beijing Information Science and Technology University, Beijing; 100192, China

Reprint Author's Address:

  • [ma, xiaoyang]school of information management, beijing information science and technology university, beijing; 100192, china

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Source :

Journal of Shanghai Jiaotong University (Science)

ISSN: 1007-1172

Year: 2019

Issue: 4

Volume: 24

Page: 477-479

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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