Software Reliability Growth Model with Partial Differential Equation for Various Debugging Processes

Xu, Jiajun; Yao, Shuzhen

doi:10.1155/2016/2476584

Cited by 11 publications

(9 citation statements)

References 42 publications

(47 reference statements)

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“…The first historical fault data set is from a web-based integrated accounting ERP system (WebERP) from August 2003 to July 2008 (Data Set 1, DS-1) [54]. This data set is one widely used data set in lots of papers, such as [55], [56]. The details are recorded in Table II and the time unit is month.…”

Section: A Data From Weberp Systemmentioning

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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Section: A Data From Weberp Systemmentioning

confidence: 99%

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

Pham

2019

IEEE Access

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“…The seventh criterion is the predicted relative variation (PRV), which is called variance [34][35][36]. It means the standard deviation of the prediction bias and is defined as:…”

Section: Of 17mentioning

confidence: 99%

“…The last criterion is the root mean square prediction error (RMSPE). It can estimate the closeness with which the model predicts the observation [34][35][36]:…”

Section: Of 17mentioning

confidence: 99%

A Software Reliability Model Considering the Syntax Error in Uncertainty Environment, Optimal Release Time, and Sensitivity Analysis

et al. 2018

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The goal set by software developers is to develop high quality and reliable software products. During the past decades, software has become complex, and thus, it is difficult to develop stable software products. Software failures often cause serious social or economic losses, and therefore, software reliability is considered important. Software reliability growth models (SRGMs) have been used to estimate software reliability. In this work, we introduce a new software reliability model and compare it with several non-homogeneous Poisson process (NHPP) models. In addition, we compare the goodness of fit for existing SRGMs using actual data sets based on eight criteria. The results allow us to determine which model is optimal.

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“…In this section, we examine models using another data collected from Tandem Computers Release #1 (Data Set 2, DS-2) [ 2 ], which has also been widely used in many studies, such as [ 7 , 42 , 44 ]. DS-2 is small in size and the failure data are tabulated in Table 4 with time unit week.…”

Section: Model Analysis With Real Applicationmentioning

confidence: 99%

A testing-coverage software reliability model considering fault removal efficiency and error generation

Pham

2017

PLoS ONE

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In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

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Software Reliability Growth Model with Partial Differential Equation for Various Debugging Processes

Cited by 11 publications

References 42 publications

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

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

A Software Reliability Model Considering the Syntax Error in Uncertainty Environment, Optimal Release Time, and Sensitivity Analysis

A testing-coverage software reliability model considering fault removal efficiency and error generation

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