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The Comparative Study of NHPP Software Reliability Model Based on Log and Exponential Power Intensity Function
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 Title & Authors
The Comparative Study of NHPP Software Reliability Model Based on Log and Exponential Power Intensity Function
Yang, Tae-Jin;
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 Abstract
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 and power intensity function (log linear, log power and exponential power), 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(), for the sake of efficient model, was employed. Analysis of failure, using real data set for the sake of proposing log and power intensity function, was employed. This analysis of failure data compared with log and power intensity 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.
 Keywords
Software Reliability;Non-Homogeneous Poisson Process;Log;Power Intensity Function;Bisection method;MSE;
 Language
Korean
 Cited by
1.
A Performance Comparative Evaluation for Finite and Infinite Failure Software Reliability Model using the Erlang Distribution, The Journal of Korea Institute of Information, Electronics, and Communication Technology, 2016, 9, 4, 351  crossref(new windwow)
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