• 품질경영학회지
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• 제27권3호
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• pp.125-141
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• 1999

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.

• 한국산업정보학회논문지
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• 제7권2호
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• pp.33-39
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• 2002

Accelerated life testing of a car is used to get information quickly on its life distribution. Test cars are no under severe conditions and fail sooner than under usual conditions. A model is fitted to the accelerated failure times and then extrapolated to estimate the life distribution under usual conditions. This paper presents an accelerated test md the reliability growth theory, and applies it to some subsystems of cars during their prototype and pilot testing. The data presented illustrates explicitly the prediction of the reliability growth in the product development cycle. The application of these techniques is a part of the product assurance function that plays an important role in product reliability improvement.

• International Journal of Reliability and Applications
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• 제8권1호
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• pp.53-66
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• 2007

This paper proposes a new non-homogeneous Poisson process software reliability growth model based on the coverage information. The new model incorporates the coverage information in the fault detection process by assuming that only the faults in the covered constructs are detectable. Since the coverage growth behavior depends on the testing strategy, the fault detection process is first modeled for the general testing strategy and then realized for the uniform testing. Finally the model for the uniform testing is empirically evaluated by applying it to real data sets.

• Structural Engineering and Mechanics
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• 제25권3호
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• pp.275-289
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• 2007

In this paper a brief overview of methods to assess the reliability of mechanical systems and structures is presented. A selection of computational procedures, stochastic structural dynamics, stochastic fatigue crack growth and reliability based optimization are discussed. It is shown that reliability based methods may form the basis for a rational decision making.

• 산업경영시스템학회지
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• 제30권3호
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• pp.127-134
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• 2007

Predicting fatigue life of rubber components is an important issue in design procedure for industrial trucks to assure the durability and reliability. Main considerations in designing rubber components against fatigue failure are the compounding technology, shape design, and manufacturing process. Among them the rubber compounding technology is one of the most critical factor to determine more than 50% of component's quality. This paper presents how to improve the durability and reliability of industrial rubber mount during its design, development and prototype testing. The data presented illustrates explicitly the prediction of reliability growth in the product development cycle. The application of these techniques is a part of the product assurance function that plays an important role in rubber components reliability improvement.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.875-881
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• 2008

This paper first discusses the existing non-homogeneous Poisson process(NHPP) software reliability growth model(SRGM) frameworks with respect to capability of representing software reliability growth phenomenon. As an enhancement of representational capability a new general coverage-based NHPP SRGM framework is developed. Issues associated with application of the new framework are then considered.

• 품질경영학회지
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• 제26권3호
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• pp.130-140
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• 1998

Studies present a guide to parameter estimation of software reliability models using SAS JMP. In this paper, we consider only software reliability growth model(SRGM), where mean value function has a S-shaped growth curve, such as Yamada et al. model, and ohba inflection model. Besides these stochastic SRGM, deterministic SRGM's, by fitting Logistic and Gompertz growth curve, have been widely used to estimate the error content of software systems. Introductions or guide lines of JMP are concerned. Estimation of parameters of Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is discussed, along with the variability in the estimates or error sum of squares. This paper have shown that JMP can be an effective tool I these research.

• 산업경영시스템학회지
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• 제36권4호
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• pp.100-108
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• 2013

In aerospace industry, MTBF (Mean Time Between Failure) and MFTBF (Mean Flight Time Between Failure) are generally used for reliability analysis. So far, especially to Korean military aircraft, MFTBF of avionic equipments is predicted by MIL-HDBK-217 and MIL-HDBK-338, however, the predicted MFTBF by military standard has a wide discrepancy to that of real-world operation, which leads to overstock and increase operation cost. This study analyzes operational data of avionic equipments. Operational MFTBF, which is calculated from operational data, is compared with predicted MFTBF calculated conventionally by military standard. In addition, failure rate trend is investigated to verify reliability growth in operational data, the investigation shows that failure rate curve from operational data has somewhat pattern with decreased failure rate and constant failure rate.

• 디지털산업정보학회논문지
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• 제10권3호
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• pp.1-9
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• 2014

The inverse Rayleigh model distribution and Rayleigh distribution model were widely used in the field of reliability station. In this paper applied using the finite failure NHPP models in order to growth model. In other words, a large change in the course of the software is modified, and the occurrence of defects is almost inevitable reality. Finite failure NHPP software reliability models can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the inverse Rayleigh and Rayleigh software reliability growth model, which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, were employed. In order to insurance for the reliability of data, Laplace trend test was employed. In many aspects, Rayleigh distribution model is more efficient than the reverse-Rayleigh distribution model was proved. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can helped.

• 한국군사과학기술학회지
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• 제20권6호
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• pp.844-852
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• 2017

Since guided weapon is high-cost and one-shot device which is non-reusable, it requires a lot of resources to prove required accuracy as a part of reliability demonstration. Once a test for proving accuracy rate of guided missile fails, it causes an additional cost and delay of schedule. This study introduces an equation for proper sample size and plan for guided-missile accuracy rate test in order to minimize the risk of test failure. Proper sample size for the test is derived by considering the reliability growth. Furthermore, each task for accuracy rate test is defined according to the development step. Therefore, this study can contribute to reduce sample size for accuracy rate test in order to meet the reliability requirement and assure transparency in the test process.