@article{ART001941660},
author={Tae-Jin Yang},
title={The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2014},
volume={9},
number={6},
pages={723-731}
TY - JOUR
AU - Tae-Jin Yang
TI - The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model
JO - Journal of Knowledge Information Technology and Systems
PY - 2014
VL - 9
IS - 6
PB - Korea Knowledge Information Technology Society
SP - 723
EP - 731
SN - 1975-7700
AB - 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. Software reliability in the final stage of the development process and the actual use test the software present in the phase failure fault number and effective time can be evaluated by the condition of the evaluation technique is important. This process can be seen as a growth process software. In this study, software managers and software failures cause a scan tool that can be utilized in the traditional software model of the Rayleigh distribution model and nonlinear regression models to compare weight model and the log-linear model was studied. As a result, the weighted regression model showed relatively efficient. 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(). In this study, the proposed non-linear regression model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. Through this study, software developers are considered by many to mean value of the function software failure mode identifying prior knowledge of how much fodder shall be able to help.
KW - Nonlinear regression model;Weight Model;NHPP;Rayleigh Distributions
DO -
UR -
ER -
Tae-Jin Yang. (2014). The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model. Journal of Knowledge Information Technology and Systems, 9(6), 723-731.
Tae-Jin Yang. 2014, "The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model", Journal of Knowledge Information Technology and Systems, vol.9, no.6 pp.723-731.
Tae-Jin Yang "The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model" Journal of Knowledge Information Technology and Systems 9.6 pp.723-731 (2014) : 723.
Tae-Jin Yang. The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model. 2014; 9(6), 723-731.
Tae-Jin Yang. "The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model" Journal of Knowledge Information Technology and Systems 9, no.6 (2014) : 723-731.
Tae-Jin Yang. The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model. Journal of Knowledge Information Technology and Systems, 9(6), 723-731.
Tae-Jin Yang. The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model. Journal of Knowledge Information Technology and Systems. 2014; 9(6) 723-731.
Tae-Jin Yang. The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model. 2014; 9(6), 723-731.
Tae-Jin Yang. "The Comparative Analysis of Software Failure Time Based on Software Reliability Model and Nonlinear Regression Model" Journal of Knowledge Information Technology and Systems 9, no.6 (2014) : 723-731.
