• Nuclear Engineering and Technology
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• 제39권2호
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• pp.129-132
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• 2007

It is generally known that software reliability growth models such as the Jelinski-Moranda model and the Goel-Okumoto's non-homogeneous Poisson process (NHPP) model cannot be applied to safety-critical software due to a lack of software failure data. In this paper, by applying two of the most widely known software reliability growth models to sample software failure data, we demonstrate the possibility of using the software reliability growth models to prove the high reliability of safety-critical software. The high sensitivity of a piece of software's reliability to software failure data, as well as a lack of sufficient software failure data, is also identified as a possible limitation when applying the software reliability growth models to safety-critical software.

• International Journal of Reliability and Applications
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• 제17권2호
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• pp.149-158
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• 2016

We are enjoying a very comfortable life thanks to modern civilization, however, comfort is not guaranteed to us. Development of software system is a difficult and complex process. Therefore, the main focus of software development is on improving the reliability and stability of a software system. We have become aware of the importance of developing software reliability models and have begun to develop software reliability models. NHPP software reliability models have been developed through the fault intensity rate function and the mean value functions within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software. In this paper, we present a new NHPP software reliability model with Burr Type III fault detection rate, and present the goodness-of-fit of the fault detection rate software reliability model and other NHPP models based on two datasets of software testing data. The results show that the proposed model fits significantly better than other NHPP software reliability models.

• 한국융합학회논문지
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• 제10권4호
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• pp.45-50
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• 2019

본 연구는 소프트웨어 신뢰성을 측정하기 위해 소프트웨어 신뢰도 측정 모형에 따라 소프트웨어 신뢰도를 측정하는 방법을 제시하려 한다. 본 연구에서 제시한 모형의 형태는 비동질적 포아송 과장의 분포를 이용하였으며, 제시된 모형의 소프트웨어 신뢰도를 측정하는 방안을 제시하였다. 제시된 모형에 따라서 적합한 소프트웨어 신뢰도 성장 모형을 선택하는 방법으로는 소프트웨어 고장 데이터에 따라서 신뢰도 함수의 추정 값에 따른 평균제곱오차를 계산하여 적합한 소프트웨어 신뢰도 함수를 제안하는 방법을 연구하였다. 본 연구에서는 소프트웨어 품질을 측정하기 위한 신뢰도 함수를 제안하기 위하여 모델을 제시하고 고장데이터를 적용하여 추정 값의 오차를 최소화하는 관점에서 소프트웨어 신뢰도 함수를 선택할 수 있는 방안을 제시한 연구로 판단된다.

• 시스템엔지니어링학술지
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• 제14권2호
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• pp.49-57
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• 2018

The main objective of this paper is come up with methodology approach for FPGA-based system in verification and validation lifecycle regarding software reliability using system engineering approach. The steps of both reverse engineering and re-engineering are carried out to implement an FPGA-based of safety critical system in Nuclear Power Plant. The reverse engineering methodology is applied to elicit the requirements of the system as well as gain understanding of the current life cycle and V&V activities of FPGA based-system. The re-engineering method is carried out to get a new methodology approach of software reliability, particularly Software Reliability Growth Model. For measure the software reliability of a given FPGA-based system, the following steps are executed as; requirements definition and measurement, evaluation of candidate reliability model, and the validation of the selected system. As conclusion, a new methodology approach for software reliability measurement using software reliability growth model is developed.

• Industrial Engineering and Management Systems
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• 제7권1호
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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.

• 인터넷정보학회논문지
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• 제7권2호
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• pp.151-160
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• 2006

소프트웨어 품질 평가 시스템과 소프트웨어 품질 평가 범위는 소프트웨어 신뢰도 측정을 위해서 정의되어져야 한다. 본 연구에서 소프트웨어 신뢰도 품질 평가 메트릭을 개발하고 소프트웨어 신뢰도를 측정하기 위한 적당한 범주의 테스팅에 대해서 조사한다. 연구를 성공적으로 수행하기 위해서 소프트웨어의 버그수를 이용해서 소프트웨어의 실패시간을 예측했다. 소프트웨어의 신뢰도는 소프트웨어의 품질 평가에 있어서 중요하다. 국제 표준 ISO/IEC 9126의 소프트웨어 신뢰성 평가 메트릭을 기초로 하여 소프트웨어 신뢰도를 평가할 수 있는 메트릭을 제안하고 이것을 중심으로 하여 평가방안을 제안한다.

• Nuclear Engineering and Technology
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• 제46권1호
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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.

• 한국자동차공학회논문집
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• 제19권4호
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• pp.1-7
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• 2011

Various convenient systems which are telecommunication and navigation system and safety system which include Antilock Braking System, Electronic Stability Control, Adaptive Cruise Control have been developed and applied to meet customer needs and each standards since vehicles are used. The complexity of new electronics become significant reason of breakdown especially embedded software failures. Hardware reliability is almost stabilized with long history but software reliability needs more improvements through reliability researches. This new challenge will improve software reliability to clear its overall failures in vehicles. This paper introduces some software reliability models and evaluates embedded software reliability using failure data which occurred during operating.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.157-161
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• 2023

Reliability is one of the computable quality features of the software. To assess the reliability the software reliability growth models(SRGMS) are used at different test times based on statistical learning models. In all situations, Tradational time-based SRGMS may not be enough, and such models cannot recognize errors in small and medium sized applications.Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into consideration in real time in order to decide the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to eradicate the problem of software reliability modeling. In the suggested model, a new distribution model is suggested to improve the reliability estimation method. We compute the model developed and stabilize its efficiency with other popular software reliability growth models from the research publication. Our assessment results show that the proposed Model is worthier to S-shaped Yamada, Generalized Poisson, NHPP.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.386-390
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• 2007

At recent times, an essential issue in the replacement of the old analogue I&C to computer-based digital systems in nuclear power plants becomes the quantitative software reliability assessment. Software reliability models have been successfully applied to many industrial applications, but have the unfortunate drawback of requiring data from which one can formulate a model. Software that is developed for safety critical applications is frequently unable to produce such data for at least two reasons. First, the software is frequently one-of-a-kind, and second, it rarely fails. Safety critical software is normally expected to pass every unit test producing precious little failure data. The basic premise of the rare events approach is that well-tested software does not fail under normal routine and input signals, which means that failures must be triggered by unusual input data and computer states. The failure data found under the reasonable testing cases and testing time for these conditions should be considered for the quantitative reliability assessment. We presented the quantitative reliability assessment methodology of safety critical software for rare failure cases in this paper.