• Journal of The Korean Association of Information Education
• /
• v.2 no.1
• /
• pp.35-51
• /
• 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 Korea Society of Industrial Information Systems
• /
• v.5 no.4
• /
• pp.39-46
• /
• 2000

New products often have in-service reliability problem despite an intensive development program. Therefore reliability data must be collected and analyzed, and improvements designed and implemented. A type of reliability incentive contract which has recently attracted a lot of attention is reliability improvement warranty(RIW). It has been employed by military, airlines, telecommunication systems, and public utilities. An RIW contract requires that the supplies carries out all repairs, modify the equipment to improve its reliability, and provides all spates needed, for a fixed period, for once-off fee. This paper presents the reliability growth analysis and management methods for in-service MC68 microprocessor, which is the main component of the base station controller in PCS(Personal Communication Service) telecommunication system. The methods will provide guidelines to monitor reliability program in planning RIW contract.

• Journal of the Korean Society for Railway
• /
• v.9 no.5 s.36
• /
• pp.606-611
• /
• 2006

This paper presents a procedure and an analysis method to evaluate reliability of the Korea high-speed train. The rolling stock system is divided into 6 sub-systems and each subsystem is classified into sub-assemblies. Functional analysis has been conducted to draw reliability block diagrams for the sub-systems. First, failure rates has been calculated for each sub-assembly from the failure data obtained during commissioning tests. Then a reliability block diagram is used to evaluate the MKBF(Mean Kilometers Before Failure) of the sub-systems. Activities to increase reliability have been carried out throughout the test runs and analysis results show that the reliability of the rolling stock system is gradually growing in time.

• Proceedings of the Korea Contents Association Conference
• /
• 2016.05a
• /
• pp.383-384
• /
• 2016

The Special Needs Education Assessment Tool (SNEAT) were verified of reliability and validity. However, the reliability and validity has been verified is only Okinawa Prefecture, the national data has not been analyzed. Therefore, this study aimed to verify the reliability and construct validity of SNEAT in Miyagi Prefecture as part of the national survey. SNEAT using 55 children collected from the classes on independent activities of daily living for children with disabilities in Miyagi Prefecture between November and December 2015. Survey data were collected in a longitudinal prospective cohort study. The reliability of SNEAT was verified via the internal consistency method; the coefficient of Cronbach's ${\alpha}$ were over 0.7. The validity of SNEAT was also verified via the latent growth curve model. SNEAT is valid based on its goodness-of-fit values obtained using the latent growth curve model, where the values of comparative fit index (0.997), tucker-lewis index (0.996) and root mean square error of approximation (0.025) were within the goodness-of-fit range. These results indicate that SNEAT has high reliability and construct validity.

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

• Nuclear Engineering and Technology
• /
• v.46 no.1
• /
• pp.55-62
• /
• 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
• /
• v.10 no.4
• /
• pp.45-50
• /
• 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.

• Journal of Applied Reliability
• /
• v.18 no.3
• /
• pp.271-279
• /
• 2018

Purpose: Probabilistic safety analysis was performed to enhance the safety and reliability of nuclear power plants because traditional deterministic approach has limitations in predicting the risk of failure by crack growth. The study introduces a probabilistic approach to establish a basis for probabilistic safety assessment of passive components. Methods: For probabilistic modeling of fatigue crack growth rate (FCGR), various FCGR tests were performed either under constant load amplitude or constant ${\Delta}K$ conditions by using heat treated X-750 at low temperature with adequate cathodic polarization. Bayesian inference was employed to update uncertainties of the FCGR model using additional information obtained from constant ${\Delta}K$ tests. Results: Four steps of Bayesian parameter updating were performed using constant ${\Delta}K$ test results. The standard deviation of the final posterior distribution was decreased by a factor of 10 comparing with that of the prior distribution. Conclusion: The method for developing a probabilistic crack growth model has been designed and demonstrated, in the paper. Alloy X-750 has been used for corrosion fatigue crack growth experiments and modeling. The uncertainties of parameters in the FCGR model were successfully reduced using the Bayesian inference whenever the updating was performed.

• Journal of Korean Society for Quality Management
• /
• v.40 no.3
• /
• pp.278-285
• /
• 2012

Purpose: Weapon systems have the long life cycle unlike the consumer product. Thus, the reliability of weapon system is improved during the life cycle through the steady technical change. In this paper, we deal with the method of evaluating the reliability of weapon system with the field failure data. Methods: Especially, we present how to gather the field failure data and evaluate the reliability through the case of K-series weapon system. To evaluate reliability, the reliability growth model is used and the result is discussed. Results: It is steadily improved the reliability of K-series weapon system deployed from 2000 to 2004. The frequency of the failures that affect the mission is largely reduced and MTBMF(mean time between mission failure) is also improved. Conclusion: We can guess the trend of the reliability of weapon system with the field data through this study. Furthermore, it can be used to improve the reliability and make maintenance policy.

• International Journal of Reliability and Applications
• /
• v.4 no.1
• /
• pp.1-12
• /
• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.