The hyper-geometric distribution software reliability growth model (HGDM): precise formulation and applicability

Jacoby, R.; Tohma, Y.

doi:10.1109/cmpsac.1990.139307

Cited by 18 publications

(4 citation statements)

References 5 publications

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“…The accumulated number of faults plotted in Dalal's work [21] presents an exponential behavior which is also observed in the Razorfish project [18]. Jacoby and Tohma's reliability growth model [22] presents cases of both exponential and S shaped curves. Additionally, Ehlrlich [23] presents examples of exponentially accumulated failure and failure intensity decay.…”

Section: Does the Stp Actually Present An Exponential Behavior?mentioning

confidence: 61%

Monitoring the software test process using statistical process control

CangussuJoão

DeCarloRaymond

MathurAditya

2003

SIGSOFT Softw. Eng. Notes

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Statistical Process Control (SPC) is a powerful tool to control the quality of processes. It assists management personnel in the identification of problems and actions to be taken to bring a process into a stable state. SPC has been applied in various fields, including the Software Development Process. However, some processes are better characterized by factors that exhibit an exponential behavior. The use of such factors for process control limits the application of traditional SPC techniques. The Software Test Process (STP) characterized by the decay in the number of remaining errors, failure intensity, and an increase in code coverage, is one such process.A variant of the traditional SPC technique is proposed. This variant uses logarithmic transformation to allow the statistical control of processes whose dominant behavior is best described by an exponential. An evaluation of the proposed transformation carried out using simulation and a case study from an industrial project, encourages the application of the proposed variant to the STP.

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Section: Does the Stp Actually Present An Exponential Behavior?mentioning

confidence: 61%

Monitoring the software test process using statistical process control

CangussuJoão

DeCarloRaymond

MathurAditya

2003

SIGSOFT Softw. Eng. Notes

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“…The HGDM has been related to the Goel-Okumoto model in a paper by Jacoby and Tohma [19], but the direct relation to the Schneidewind model that is more obvious and interesting, has not been mentioned.…”

Section: The Tohma Hgdm Modelmentioning

confidence: 97%

“…N,(t). (19) It can be seen that the fault correction process (N,(t),tXI] is just the thinning process of /Nd(t),tM].…”

Section: 3: Application Of the Theory Of Poisson Thinningmentioning

confidence: 99%

The Schneidewind software reliability model revisited

Xie

Zhao

[1992] Proceedings Third International Symposium on Software Reliability Engineering

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A software reliability model based on nonhomogeneous Poisson process {NHPP) was proposed in Schneidewind[ I ] in 1975. This model seems to be the first of this kind. Since then, many other NHPP models have been suggested and studied by various authors. Surprisingly, the paper by Schneidewind has not been widely referred to. In this paper, we show that several NHPP models can be derived based on the general assumptions made by Schneidewind. Also, we note that in the original paper, there are several interesting approaches worth further consideration. To mention a few, Schi&wind modelled both the software correction process and the software detection process. Methods of weighted least squares were adopted together with the software reliability forecasting based on previous measurements. In this paper, the Schneidewind model is revisited and some further research results are presented 1: IntroductionThe software failure process is often described by a nonhomogeneous Poisson process (NHPP). There are many papers discussing this type of models, see for example [2-91 for a general treatment of NHPP software reliability models. It seems that the first NHPP software reliability model was published by Schneidewind [l] where an NHPP is used to model the occurrence of faults during software testing.Since then many software reliability models have been proposed and studied. However, the paper by Schneidewind has not been widely referred to. In this paper, we first review the most important contributions by Schneidewind and show that many other NHPP models can be derived based on the assumptions made in fax: (46) 13 28 2742 the original paper by Schneidewind [l]. Also, we note that there are several other interesting appaches studied by Schneidewind worth further considemtion.To name some of them, Schneidewind modelled both the software fault detection process and the software fault correction process. The latter one is assumed to be a delayed fault detection process. We feel that as one of the earliest NHPP software reliability models, the Schneidewind model is an important one in the family of software reliability models. In Schneidewind [l], methods of weighted least squares are used together with some software reliability pmliction methodologies. This is also an important topic in the application of software reliability models. In this paper, the Schneidewind model is revisited and some research directions are furtber discussed. In Section 2, the essential contributions in the Schneidewind paper are reviewed and we unify some other existing software reliability models that are closely related to the Schneidewind model. In Section 3, we study some modelling approaches continuing that presented in the paper by Schneidewind, especially the modelling of fault-correction process. Finally, in Section 4 we discuss some related estimation and prediction problems such as the use of incomplete data and the methods of weighted least squares. 2: The Schneidewind model and the relation to some other modelsThe Schneidewind model is m...

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“…Therefore, Eq. ( 8) can be extended to be (9) Considering the case that ui is not a constant, E-wi = mp,,(1 -a > 0, 0 < p,, 5 1.…”

Section: Learning Factor With Exponentialmentioning

confidence: 99%

Applying various learning curves to hyper-geometric distribution software reliability growth model

Hou

Kuo

Chang

Proceedings of 1994 IEEE International Symposium on Software Reliability Engineering

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The Hyper-Geometric Distribution software reliability growth Model (HGDM) has been shown to be able to estimate the number of faults initially resident in a program at the beginning of the test-and-debug phase.A key factor of the HGDM is the "sensitivity factar'', which represents the number of faults discovered and rediscovered at the application of Q test instance. The learning curve incorporated in the sensitivity factor is generally assumed to be linear in the literature. However, this assumption is apparently not realistic in many applications. We propose two new sensitivity factors based on the exponential learning curve and the S-shaped learning curve, respectively. Furthermore, the growth curves of the cumulative number of discovered faults for the HGDM with the proposed learning curves are investigated. Extensive experiments have been performed based on two real test/debug data sets, and the results show that the HGDM with the proposed learning curves estimates the number of initial faults better than previous approaches.

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The hyper-geometric distribution software reliability growth model (HGDM): precise formulation and applicability

Cited by 18 publications

References 5 publications

Monitoring the software test process using statistical process control

Monitoring the software test process using statistical process control

The Schneidewind software reliability model revisited

Applying various learning curves to hyper-geometric distribution software reliability growth model

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