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Appl. Sci. 2017, 7(10), 983; doi:10.3390/app7100983

A Software Reliability Model with a Weibull Fault Detection Rate Function Subject to Operating Environments

1
Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero Dong-gu, Gwangju 61452, Korea
2
Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USA
*
Author to whom correspondence should be addressed.
Received: 21 August 2017 / Revised: 20 September 2017 / Accepted: 22 September 2017 / Published: 25 September 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environments; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. As such, it is difficult to improve software reliability for a variety of reasons, such as a given environment, or a bug location in code. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness of fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent. View Full-Text
Keywords: non-homogeneous Poisson process; software reliability; Weibull function; mean squared error non-homogeneous Poisson process; software reliability; Weibull function; mean squared error
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Song, K.Y.; Chang, I.H.; Pham, H. A Software Reliability Model with a Weibull Fault Detection Rate Function Subject to Operating Environments. Appl. Sci. 2017, 7, 983.

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