Optimal Release Time and Sensitivity Analysis Using a New NHPP Software Reliability Model with Probability of Fault Removal Subject to Operating Environments

Song, Kiwon; Chang, In Hong; Pham, Hoang

doi:10.3390/app8050714

Cited by 15 publications

(4 citation statements)

References 33 publications

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“…Among all the models, the NHPP models can be regarded as the most effective [38,20,[39][40][41]. It is proposed by Goel and Okumoto in 1979, and their framework of NHPP models is called Goel-Okumoto (GO) model [42].…”

Section: A Assumptionmentioning

confidence: 99%

Simulated Software Testing Process and Its Optimization Considering Heterogeneous Debuggers and Release Time

Gao

2021

IEEE Access

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Most traditional software reliability growth models (SRGMs) assume immediate fault correction upon detection and therefore only consider fault detection process (FDP). In order to be more realistic, some researchers have tried to incorporate fault correction process (FCP) and fault introduction process (FIP) into the software reliability models. However, it is still difficult to incorporate into the analytical software reliability models some other factors, such as the different fault detection and correction capabilities of debuggers. In this paper, a simulation approach is proposed to model FDP, FIP, and FCP together considering debuggers with different contributions to fault detection rate, different fault correction rate and different fault introduction rate. Besides, this paper also constructed a cost calculation method to optimize the testing design including debuggers assignment and software release time. Some numerical examples are provided to illustrate the proposed model. The results show that the trends of FDP, FCP and FIP are consistent with the intuition to the practice of software testing, and the optimal testing resources allocation and the optimal release time can be obtained according to the proposed model.

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Section: A Assumptionmentioning

confidence: 99%

Simulated Software Testing Process and Its Optimization Considering Heterogeneous Debuggers and Release Time

Gao

2021

IEEE Access

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“…In the early 2000s, researchers began to explore new approaches such as the application of calibration factors to the software reliability model considering the uncertainty of the operating environment [11][12][13]. Recently, an NHPP software reliability model considering the uncertainty of the software operating environment has been proposed [14][15][16][17][18][19]. In addition, many testing coverage functions have been proposed in terms of different distributions, and software reliability models based on different testing coverage function have also been developed [2,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis

2019

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We have been attempting to evaluate software quality and improve its reliability. Therefore, research on a software reliability model was part of the effort. Currently, software is used in various fields and environments; hence, one must provide quantitative confidence standards when using software. Therefore, we consider the testing coverage and uncertainty or randomness of an operating environment. In this paper, we propose a new testing coverage model based on NHPP software reliability with the uncertainty of operating environments, and we provide a sensitivity analysis to study the impact of each parameter of the proposed model. We examine the goodness-of-fit of a new testing coverage model based on NHPP software reliability and other existing models based on two datasets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models. In addition, the results for the sensitivity analysis show that the parameters of the proposed model affect the mean value function.

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“…Recently, we have witnessed several works continuously developed by Song et al in this direction, i.e. they gave a three-parameter fault-detection rate function and assumed that the environmental factor had the exponential distribution [40], a Weibull fault detection rate [41], an S-shaped fault detection rate [42], a fault detection rate function affected by the probability of fault removal on a failure [43], a Weibull failure detection rate function with consideration of testing time with syntax error [44] and an inflection S-shaped fault detection rate [45]. Meanwhile, the last four models all assumed the Gamma distribution environmental factor and a constant expected number of faults existing in the software before testing.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments

Pham

2019

IEEE Access

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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. INDEX TERMS Generalized software reliability growth model, non-homogeneous Poisson process, uncertainty, operating environment, imperfect debugging.

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Optimal Release Time and Sensitivity Analysis Using a New NHPP Software Reliability Model with Probability of Fault Removal Subject to Operating Environments

Cited by 15 publications

References 33 publications

Simulated Software Testing Process and Its Optimization Considering Heterogeneous Debuggers and Release Time

Simulated Software Testing Process and Its Optimization Considering Heterogeneous Debuggers and Release Time

A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis

A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments

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