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.
Highlights
Software systems have become an essential part of our lives
Many existing non-homogeneous Poisson process (NHPP) software reliability models have been developed through the fault intensity rate function and the mean value functions m(t) within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of software
Existing models are applied to software testing data and used to make predictions on the software failures and reliability in the field
Summary
Received: August 2017; Accepted: September 2017; Published: 25 September 2017. Featured Application: This study introduces a new software reliability model with the Weibull fault detection rate function that takes into account the uncertainty of operating environments.
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