• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.55-62
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• 2014

A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM), where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.45-50
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• 2019

This study proposes a method to measure software reliability according to software reliability measurement model to measure software reliability. The model presented in this study uses the distribution of Non - Homogeneous Poisson Process and presents a measure of the software reliability of the presented model. As a method to select a suitable software reliability growth model according to the presented model, we have studied a method of proposing an appropriate software reliability function by calculating the mean square error according to the estimated value of the reliability function according to the software failure data. In this study, we propose a reliability function to measure the software quality and suggest a method to select the software reliability function from the viewpoint of minimizing the error of the estimation value by applying the failure data.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.51-56
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• 2008

With growing demand for zero defects, predicting reliability of software systems is gaining importance. Software reliability models are used to estimate the reliability or the number of latent defects in a software product. Most reliability models to estimate the reliability of software in the literature are based on the development lifecycle stages. However, in the maintenance phase, the software needs to be corrected for errors and to be enhanced for the requests from users. These decrease the reliability of software. Software Reliability Growth Models (SRGMs) have been applied successfully to model software reliability in development phase. The software reliability in maintenance phase exhibits many types of systematic or irregular behaviors. These may include cyclic behavior as well as long-term evolutionary trends. The cyclic behavior may involve multiple periodicities and may be asymmetric in nature. In this paper, SGRM has been adapted to develop a reliability prediction model for the software in maintenance phase. The model is established using maintenance data from a commercial shop floor control system. The model is accepted to be used for resource planning and assuring the quality of the maintenance work to the user.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.273-273
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• 2002

In this paper, we investigate a software release policy with software reliability growth factor during the warranty period by assuming that the software reliability growth is assumed to occur after the testing phase with probability p and the software reliability growth is not assumed to occur after the testing phase with probability 1-p. The optimal release policy to minimize the expected total software cost is discussed. Numerical examples are shown to illustrate the results of the optimal policy. And we consider a Bayesian decision theoretic approach to determine an optimal software release policy. This approach enables us to update our uncertainty when determining optimal software release time, When the failure time is Weibull distribution with uncertain parameters, a bayesian approach is established. Finally, numerical examples are presented for illustrative propose.

• Journal of The Korean Association of Information Education
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• v.2 no.1
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• pp.35-51
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• 1998

Software reliability growth model is one of the evaluation methods, software quality which quantitatively calculates the software reliability based on the number of errors detected. For correct and precise evaluation of reliability of certain software, the reliability model, which is considered to fit dose to real data should be selected as well. In this paper, the optimal model for specific test data was selected one of among five software reliability growth models based on NHPP(Non Homogeneous Poission Process), and in result reliability estimating scales(total expected number of errors, error detection rate, expected number of errors remaining in the software, reliability etc) could obtained. According to reliability estimating scales obtained, Software development and predicting optimal release point and finally in conducting systematic project management.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.837-844
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• 2002

In general, software test is carried out to detect or repair errors in system during software development process. Namely, we can evaluate software reliability through collecting and removing the faults detected in testing phase. Software reliability growth model evaluates reliability of software mathematically. Many kinds of software reliability growth modeling which modeling the processes of detecting, revising and removing the faults detected in testing phase have been proposed in many ways. and, it is assumed that almost of these modeling have one typed detect and show the uniformed detection rate. In this study, significance levels of the faults detected in test phase are classified according to how they can affect on the whole system and then the fault detection capability of them is applied. From this point of view, We here by propose a software reliability growth model with faults detection capability according considering fault significance levels and apply some fault data to this proposed model and finally verify its validity by comparing and estimating with the existing modeling.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.89-95
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• 2008

In this paper, we propose a software reliability growth model based on the superposition cause in the software system, which is isolated by the executed test cases in software testing. In particular, our model assumes an imperfect debugging environment in which new faults are introduced in the fault-correction process, and is formulated as a nonhomogeneous Poisson process(NHPP). Further, it is applied to fault-detection data, the results of software reliability assessment are shown, and comparison of goodness-of-fit with the existing software reliability growth model is performed.

• 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.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.909-916
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• 2010

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1451-1458
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• 2004

Current software reliability growth models based on Gompertz growth curve are all logarithmic type. Software reliability growth models based on logarithmic type Gompertz growth curve has difficulties in parameter estimation. Therefore this paper proposes a software reliability growth model based on the logistic type Gompertz growth curie. Its usefulness is empirically verified by analyzing the failure data sets obtained from 13 different software projects. The parameters of model are estimated by linear regression through variable transformation or Virene's method. The proposed model is compared with respect to the average relative prediction error criterion. Experimental results show that the pro-posed model performs better the models based on the logarithmic type Gompertz growth curve.