• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1361-1369
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• 2008

The analysis of software reliability model provides the means to analysts, software engineers, and systems analysts and developers who want to predict, estimate, and measure failure rate of occurrences in software. In this paper, reliability growth model, in which the operating time between successive failure is a continuous random variable, is proposed. This model is based on order statistics of two parameters Burr type XII distribution. We propose the measure based on U-plot. Also the performance of the suggested model is tested on real data set.

• Convergence Security Journal
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• v.13 no.1
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• pp.3-10
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• 2013

In this paper, reliability software model considering fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the S-shaped distribution model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model was used. In a software failure data analysis considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of failure time data and reliability make out.

• Nuclear Engineering and Technology
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• v.29 no.6
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• pp.433-443
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• 1997

In this work, an analytic approach to the dependability of software in the operational phase is suggested with special attention to the hardware fault effects on the software behavior : The hardware faults considered are memory faults and the dependability measure in question is the reliability. The model is based on the simple reliability theory and the graph theory which represents the software with graph composed of nodes and arcs. Through proper transformation, the graph can be reduced to a simple two-node graph and the software reliability is derived from this graph. Using this model, we predict the reliability of an application software in the digital system (ILS) in the nuclear power plant and show the sensitivity of the software reliability to the major physical parameters which affect the software failure in the normal operation phase. We also found that the effects of the hardware faults on the software failure should be considered for predicting the software dependability accurately in operation phase, especially for the software which is executed frequently. This modeling method is particularly attractive for the medium size programs such as the microprocessor-based nuclear safety logic program.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.33-40
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• 1994

Most software reliability models and other methods attempt to estimate some measures based on its fault history. There are several phases of the software life cycle including testing phase. We can propose it's stopping rule to decide when to stop the testing and pass it on to the next phase by considering the detailed structure of software and calculating the failure rate when each fault was detected. Downs (1985) proposed a method which was developed for estimating the failure rate applicable only to two-level profiles. In this paper, I extended to profiles involving more levels.

• International journal of advanced smart convergence
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• v.1 no.1
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• pp.57-60
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• 2012

In this paper, we have performed a safety analysis on an automotive evaporator inspection system. We performed the bi-directional analysis on the manufacturing line. Software Fault Tree Analysis (SFTA) as backward analysis and Software Failure Modes, Effects, & Criticality Analysis (SFMECA) as forward analysis are performed alternately to detect potential cause-to-effect relations. The analysis results indicate the possibility of searching and summarizing fault patterns for future reusability.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.26-37
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• 2003

An optimal software release, which is related to the development cost, error detection and correction under the various operation systems, is a critical factor for managing project. This paper described optimal software release issues to predict the release time of large switching system with the system stability point of view and evaluated a timely supply of target system, proper utilization of resources under the software reliability valuation basis. Finally, Using initial failure data, based on the exponential reliability growth model methodology, optimal release time, and analysis of failure data during the system testing and managing methodologies were presented.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Convergence Security Journal
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• v.6 no.3
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• pp.145-153
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• 2006

Infinite failure NHPP models for a record value satisfies mode proposed in the literature exhibit either monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, propose comparative study of software reliability model using Erlang distribution, Rayleigh and Gumbel distribution. Equations to estimate the parameters using maximum likelihood estimation of infinite failure NHPP model based on failure data collected in the form of interfailure times are developed. For the sake of proposing distribution, we used to the special pattern. Analysis of failure data set using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

• Convergence Security Journal
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• v.11 no.3
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• pp.19-24
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• 2011

Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.