Incorporating inflection S-shaped fault reduction factor to enhance software reliability growth

Pachauri, Bhoopendra; Dhar, Joydip; Kumar, Ajay

doi:10.1016/j.apm.2014.08.006

Cited by 48 publications

(18 citation statements)

References 25 publications

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“…Practically, FRF is affected by various factors viz, resource allocation, defect density, fault dependency, environment, time lag in removing faults, learning process, error generation etc. Pachauri, Dhar and Kumar [19] defined FRF as S-shaped function for software released in multiple versions. Several other definitions of FRF were coined by some researchers which did not become much popular in the research community Friedman, Tran and Goddard [20] defined FRF in terms of detectability, associability and fault growth and Malaiya, Von Mayrhauser and Srimani [21] defined FRF in terms of fault exposure ratio.…”

Section: Fig 1 Change Point Representationmentioning

confidence: 99%

“…Among these definitions, Musa's version of FRF became very popular in the research community. After Realizing the importance of FRF in affecting the failure behavior, several NHPP based SRGMs were proposed considering FRF as one of the key component [6,8,9,18,19,48]. These studies also captured the FRF trend.…”

Section: Frfmentioning

confidence: 99%

“…They also plotted the FRF trends followed by each dataset. Pachauri, Dhar and Kumar [19]considered Inflexion S-Shaped FRF to increase accuracy of growth model. The authors also discussed the multi-release model considering the proposed FRF.…”

Section: Frfmentioning

confidence: 99%

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Testing Resource based Optimal Release Policy for Software System Incorporating Fault Reduction Factor and Change point

Arora,

Tandon,

Aggarwal

et al. 2019

IJRTE

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In today’s competitive scenario, it is essential for software developers to perform rigorous testing. It helps them to satisfy the demand for reliable software systems. Various external and internal factors like human expertise, fault dependency, code complexity, dynamic approaches etc. affect the process of fault removal. Hence, along the time-line fault removal rate may change. The point on time-line beyond which rates are altered is termed as the change point. Also in many practical situations, the number of failures experienced may not coincide with the number of faults removed from the system. This ratio is computed by Fault Reduction Factor (FRF). Here, we have proposed testing effort based model considering effort-dependent FRF with and without change point for gauging the failure pattern of a software system. The FRF has been modelled by logistic curve. The developed models have been verified using real-life software fault datasets. Model parameters are estimated and various performance criteria are employed to check the goodness of fit. Later, we have developed a software cost model to determine the optimal Release testing effort that minimizes total expected cost of fault removal during testing phase and operational phase of software life cycle subject to a reliability constraint. A numerical study has been also taken to demonstrate the results. Cost sensitivity analysis has been carried out to identify the crucial cost component and role of each cost component on optimal testing effort and overall cost of development.

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Section: Fig 1 Change Point Representationmentioning

confidence: 99%

Section: Frfmentioning

confidence: 99%

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Testing Resource based Optimal Release Policy for Software System Incorporating Fault Reduction Factor and Change point

Arora,

Tandon,

Aggarwal

et al. 2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…process [16], [17]. Also some other factors in real developing process are integrated to improve the estimation accuracy of SRGMs, such as testing coverage [18]- [20], testing effort [21]- [25], time-delay fault correction [26]- [29] and fault reduction factor [30]. 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.…”

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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“…Hsu et al [17] proposed srgm by considering time variable frf that depicts increasing, decreasing and constant patterns. The general model is defined as: where and represent frf and fdr respectively Later on Pachauri et al [18] described frf as inflexion S-Shaped function for a multi-release software system. Chatterjee et al [19] formulated the Weibull-type frf under perfect and imperfect debugging process.…”

Section: Introductionmentioning

confidence: 99%

2-Dimensional Multi-Release Software Reliability Modelling considering Fault Reduction Factor under imperfect debugging

2018

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Introduction: The present research was conducted at the University of Delhi, India in 2017.Methods: We develop a software reliability growth model to assess the reliability of software products released in multiple versions under limited availability of resources and time. The Fault Reduction Factor (frf) is considered to be constant in imperfect debugging environments while the rate of fault removal is given by Delayed S-Shaped model.Results: The proposed model has been validated on a real life four-release dataset by carrying out goodness of fit analysis. Laplace trend analysis was also conducted to judge the trend exhibited by data with respect to change in the system’s reliability.Conclusions: A number of comparison criteria have been calculated to evaluate the performance of the proposed model relative to only time-based multi-release Software Reliability Growth Model (srgm).Originality: In general, the number of faults removed is not the same as the number of failures experienced in given time intervals, so the inclusion of frf in the model makes it better and more realistic. A paradigm shift has been observed in software development from single release to multi release platform.Limitations: The proposed model can be used by software developers to take decisions regarding the release time for different versions, by either minimizing the development cost or maximizing the reliability and determining the warranty policies.

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Incorporating inflection S-shaped fault reduction factor to enhance software reliability growth

Cited by 48 publications

References 25 publications

Testing Resource based Optimal Release Policy for Software System Incorporating Fault Reduction Factor and Change point

Testing Resource based Optimal Release Policy for Software System Incorporating Fault Reduction Factor and Change point

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

2-Dimensional Multi-Release Software Reliability Modelling considering Fault Reduction Factor under imperfect debugging

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