Abstract
This paper proposes a generalized model to cover imperfect debugging and the uncertainty of the operating environment and its effect on fault detection rate into software reliability evaluation based on a non-homogeneous Poisson process (NHPP). Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures over the past 40 years, but most of these models assume that the operating environment is the same as the testing environment. However, in fact, due to the unpredictability of the uncertain factors in the operating environments for the software, they may considerably influence the software's reliability in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the original reliability prediction in the testing phase of the software development process, also from all its similar applications in other fields. In this paper, a general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on. Several published models are shown to be covered by this general model and a new model is also developed and examined. The numerical illustrative examples of the proposed model have been validated on two sets of real software failure data in terms of six criteria. The comparison results demonstrate that the new model can fit and predict significantly better than other existing models.
Highlights
Software reliability has become one of the most important customer-oriented attributes of software quality [1]
Most models assume that the operating environment is the same as the testing environment, and the underlying assumption is that the software used in the operating environment has the same failure-occurrence behavior as that used in the software testing environment
In this paper, we propose a generalized software reliability model considering the uncertainty of field environments based on non-homogeneous Poisson process (NHPP)
Summary
Software reliability has become one of the most important customer-oriented attributes of software quality [1]. Teng and Pham firstly proposed a software gain model under the random operating environment with consideration of the effect of the random field environmental factor on the cost model [34] They proposed a model that discusses the randomness of the environment and its effects on the fault detection rate under the condition that the uncertainty of operating environmental effect follows the Gamma (or Beta) distribution and fault content function is a linear function of the mean value function [35]. Pham developed a Vtub-shaped software fault detection rate model considering the randomness of the operating environment where the fault-detection rate follows a Vtub-shaped function under the condition that the uncertainty follows the Gamma distribution and fault content remains a constant [37]. Chang et al introduced the testing coverage into software reliability model considering the uncertainty of operating environment, assumed that the uncertainty follows the Gamma distribution and the total fault number remains the same [38]. A constant h(t) means that failure intensity is proportional to the number of remaining faults, and an increasing h(t) means an increasing fault detection rate due to testing learning or an S-shaped h(t) attributed to fluctuations during the testing process [16], [17], or a combination of both mentioned above
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