A moving average non-homogeneous Poisson process reliability growth model to account for software with repair and system structures

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21 Citations (Scopus)

Abstract

We develop a moving average non-homogeneous Poisson process (MA NHPP) reliability model which includes the benefits of both time domain, and structure based approaches. This method overcomes the deficiency of existing NHPP techniques that fall short of addressing repair, and internal system structures simultaneously. Our solution adopts a MA approach to cover both methods, and is expected to improve reliability prediction. This paradigm allows software components to vary in nature, and can account for system structures due to its ability to integrate individual component reliabilities on an execution path. Component-level modeling supports sensitivity analysis to guide future upgrades, and updates. Moreover, the integration capability is a benefit for incremental software development, meaning only the affected portion needs to be re-evaluated instead of the entire package, facilitating software evolution to a higher extent than with other methods. Several experiments on different system scenarios and circumstances are discussed, indicating the usefulness of our approach.

Original languageEnglish (US)
Pages (from-to)411-421
Number of pages11
JournalIEEE Transactions on Reliability
Volume56
Issue number3
DOIs
StatePublished - Sep 1 2007

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Repair
Software packages
Sensitivity analysis
Software engineering
Experiments

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

Cite this

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abstract = "We develop a moving average non-homogeneous Poisson process (MA NHPP) reliability model which includes the benefits of both time domain, and structure based approaches. This method overcomes the deficiency of existing NHPP techniques that fall short of addressing repair, and internal system structures simultaneously. Our solution adopts a MA approach to cover both methods, and is expected to improve reliability prediction. This paradigm allows software components to vary in nature, and can account for system structures due to its ability to integrate individual component reliabilities on an execution path. Component-level modeling supports sensitivity analysis to guide future upgrades, and updates. Moreover, the integration capability is a benefit for incremental software development, meaning only the affected portion needs to be re-evaluated instead of the entire package, facilitating software evolution to a higher extent than with other methods. Several experiments on different system scenarios and circumstances are discussed, indicating the usefulness of our approach.",
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