NHPP software reliability model with inflection factor of the fault detection rate considering the uncertainty of software operating environments and predictive analysis

Kwang Yoon Song, In Hong Chang, Hoang Pham

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The non-homogeneous Poisson process (NHPP) software has a crucial role in computer systems. Furthermore, the software is used in various environments. It was developed and tested in a controlled environment, while real-world operating environments may be different. Accordingly, the uncertainty of the operating environment must be considered. Moreover, predicting software failures is commonly an important part of study, not only for software developers, but also for companies and research institutes. Software reliability model can measure and predict the number of software failures, software failure intervals, software reliability, and failure rates. In this paper, we propose a new model with an inflection factor of the fault detection rate function, considering the uncertainty of operating environments and analyzing how the predicted value of the proposed new model is different than the other models. We compare the proposed model with several existing NHPP software reliability models using real software failure datasets based on ten criteria. The results show that the proposed new model has significantly better goodness-of-fit and predictability than the other models.

Original languageEnglish (US)
Article number521
JournalSymmetry
Volume11
Issue number4
DOIs
StatePublished - Apr 1 2019

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Chemistry (miscellaneous)
  • Mathematics(all)
  • Physics and Astronomy (miscellaneous)

Keywords

  • Fault detection rate
  • Non-homogeneous Poisson process
  • Predictive analysis
  • Software failure
  • Software reliability model

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