• International Journal of Reliability and Applications
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• v.6 no.2
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• pp.87-99
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• 2005

Coverage-based software reliability growth models (SRGMs) have been developed and successfully applied in practice. Performance of a coverage-based SRG M depends on the coverage function employed by the SRGM. When the coverage function represents the coverage growth behavior well irrespective of type of the testing profile the corresponding coverage-based SRGM is expected to be widely applicable. This paper first conducts a study of selecting the most representative coverage functions among the available coverage functions. Then their performances are empirically evaluated and compared. The result provides a foundation for developing widely applicable coverage-based SRGMs and monitoring the progress of a testing process.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.395-400
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• 1997

An important quality characteristic of a software reliability.Software reliablilty growh models prvied the tools to evluate and moniter the reliabolty growth behavior of the sofwate during the testing phase Therefore failure data collected during the testing phase should be continmuosly analyzed on the basis of some selected software reliability growth models.For the cases where nonhomogeneous Poisson proxess models are the candiate models,we suggest Poisson regression model, which expresses the relationship between the expeted and actual failures counts in disjonint time intervals,for analyzing the failure count data.The weighted lest squares method is then used to-estimate the paramethers in the parameters in the model:The resulting estimators are equivalent to the maximum likelihood estimators. The method is illustrated by analyzing the failutr count data gathered from a large- scale switchong system.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.409-420
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• 2001

Recently a new approach to evaluation of software reliability, one of important attributes of a software system, during testing has been devised. This approach utilizes test coverage information. The coverage-based software reliability growth models recently appeared in the literature are first reviewed and classified into two classes. Inherent problems of each of the two classes are then discussed and their validity is empirically investigated. In addition, a new mean value function in coverage and a heuristic procedure for selecting the best coverage are proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1915-1925
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• 1998

The switching system software is large scale, real-time multi-task system which requires high reliability. The reliability assessment of large-scale software is very important for the success of software development project. For this raeson, the software quality measurement is much more important. In this paper, we have learned about the software reliability, metho of the analysis of failure data and estimation of software quality. To estimate the software reliability, using the failure data found during of the system test. We apply the two software reliability growth models, named Goel-Okumoto(G-O) and S-shaped model, to estimate the software reliability. Also, we compared with the results and we reviewed fully not only development cycle but validation and verification of the test data, for each software versions. This paper presents a software reliability model that suitale the software development project and the activeity of quality control for the switching system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.19-27
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• 2014

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the reliability model with log type mean value function (Musa-Okumoto and log power 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, was employed. Analysis of failure using real data set for the sake of proposing log type mean value function was employed. This analysis of failure data compared with log type mean value function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the log type model is also efficient in terms of reliability because it (the coefficient of determination is 70% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.1-12
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• 2006

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimator and maximum likelihood estimator for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.231-241
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• 1996

The reliability in software has expand in quality and quantity, also its importance and role are increased. But, a study of software reliability is lack of development. this paper software reliability growth models(SRGM) described by NonHome-geneous Poisson(NHPP)processes. Using actual software error data observed by software testing the SRGM's are composition of error-class, and error-class by three class. this paper made the reliability-model of software using three error- class. The purpose of this study to increase software productivity and to improve software quality. So to achive these goals we focused a study of software reliability model using the error-class.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.195-210
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• 1996

For predicting the parameters and estimating the goodness of fit reliability growth model based on NHPP(Non Homogeneous Poission Process) among various reliability growth models, a Delayed S Shaped SRGM Tool is designed and Implemented. The Implemented tool is applied to real software error data, and the result Is compared and annalized.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.813-823
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• 2008

The non-homogeneous Poisson process(NHPP) based software reliability growth models are proved quite successful in practical software reliability engineering. The fault detection rate is usually assumed to be the continuous and monotonic function. However, the fault detection rate can be affected by many factors such as the testing strategy, running environment and resource allocation. This paper describes a parameter estimation of software reliability growth model with change-point problem. We obtain the maximum likelihood estimate(MLE) and least square estimate(LSE), and compare goodness-of-fit.

• 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.