• International Journal of Reliability and Applications
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• v.6 no.2
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• pp.87-99
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• 2005

Coverage-based software reliability growth models (SRGMs) have been developed and successfully applied in practice. Performance of a coverage-based SRG M depends on the coverage function employed by the SRGM. When the coverage function represents the coverage growth behavior well irrespective of type of the testing profile the corresponding coverage-based SRGM is expected to be widely applicable. This paper first conducts a study of selecting the most representative coverage functions among the available coverage functions. Then their performances are empirically evaluated and compared. The result provides a foundation for developing widely applicable coverage-based SRGMs and monitoring the progress of a testing process.

• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.835-842
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• 2001

We propose a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase. The time-dependent behavior of testing effort expenditures is described by a Weibull curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, we discuss the relations between testing time and reliability and between duration following failure fixing and reliability are studied in this paper. The release time making the testing cost to be minimum is determined through studying the cost for each condition. Also, the release time is determined depending on the conditions of the specified reliability. The optimum release time is determined by simultaneously studying optimum release time issue that determines both the cost related time and the specified reliability related time.

• Convergence Security Journal
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• v.7 no.2
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• pp.1-8
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault (intensity function). In this paper, intensity function of Goel-Okumoto model was reviewed, proposes Kappa (2) and the Burr distribution, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method. For model determination and selection, explored goodness of fit (the error sum of squares) The methodology developed in this paper is exemplified with a software reliability real data set introduced by NTDS (Naval Tactical Data System)

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.119-126
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• 2010

The software reliability growth model(SRGM) has been developed in order to estimate such reliability measures as remaining fault number, failure rate and reliability for the developing stage software. Almost of them assumed that the faults detected during testing were eventually removed. Namely, they have studied SRGM based on the assumption that the faults detected during testing were perfectly removed. The fault removing efficiency, however, is imperfect and it is widely known as so in general. It is very difficult to remove detected fault perfectly because the fault detecting is not easy and new error may be introduced during debugging and correcting. Therefore, We want to study imperfect software testing effort for the logistic testing effort which is thought to be the most adequate in this paper.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.61-73
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• 2009

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or Logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimater and maximum likelihood estimater for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Journal of Korean Society for Quality Management
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• v.37 no.3
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• pp.33-38
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• 2009

When we estimate the parameters of software reliability models, we usually use maximum liklihood estimator(MLE). But this method is required a large data set. In particular, when we want to estimate it with small observed data such as early stages of testing, we give rise to the non-existence of MLE. Therefore, it is interesting to look into the influence of parameter estimators obtained using MLE. In this paper, we use two non-homogenous poisson process software reliability growth model: delayed S-shaped model and log power model. In this paper, we calculate the sensitivity of estimators about failure intensity function for two SRGMs respectively.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.170-178
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• 2011

In general, a software fault detection phenonenon is described by a software reliability model based on a nonhomogeneous Poisson process(NHPP). In this paper, we propose a software reliability growth model considering the differences of the software environments in both the testing phase and the operational phase. Also, we consider the problem of determining the optimal release time and the optimal warranty period that minimize the total expected software cost which takes account of periodic software maintenance(e.g. patch, update, etc). Finally, we analyze the sensitivity of the optimal release time and warranty period based on the fault data observed in the actual testing process.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.14-22
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• 1994

This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.497-506
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• 2007

Coverage-based NHPP SRGMs have been introduced in order to incorporate the coverage growth behavior into the NHPP SRGMs. The coverage growth function representing the coverage growth behavior during testing is thus an essential factor of the coverage-based NHPP SRGMs. This paper proposes a class of discrete time coverage growth functions and illustrates its application to real data sets.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.5
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• pp.11-16
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• 1998

We analyze the software reliability growth models for the specified period from the viewpoint of theory of differential equations. we defien a genralized form of reliability growth models as follws: dN(t)/dt = b(t)f(N(t)), Where N(t) is the number of remaining faults and b(t) is the failure rate per software fault at time t. We show that the well-known three software reliability growth models - Goel - Okumoto, s-shaped, and Musa-Okumoto model- are special cases of the generalized form. We, also, extend the generalized form into an extended form being dN(t)/dt = b(t, .gamma.)f(N(t)), The genneralized form can be obtained if the distribution of failures is given. The extended form can be used to describe a software reliabilit growth model having weibull density function as a fault exposure rate. As an application of the generalized form, we classify three mentioned models according to the forms of b(t) and f(N(t)). Also, we present a case study applying the generalized form.