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Reseach Article

A Bivariate Autoregressive Software Reliability Model

by K.Vedavathi, K.Srinivas Rao, A.VinayBabu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 7
Year of Publication: 2012
Authors: K.Vedavathi, K.Srinivas Rao, A.VinayBabu
10.5120/4699-6850

K.Vedavathi, K.Srinivas Rao, A.VinayBabu . A Bivariate Autoregressive Software Reliability Model. International Journal of Computer Applications. 38, 7 ( January 2012), 19-22. DOI=10.5120/4699-6850

@article{ 10.5120/4699-6850,
author = { K.Vedavathi, K.Srinivas Rao, A.VinayBabu },
title = { A Bivariate Autoregressive Software Reliability Model },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 7 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number7/4699-6850/ },
doi = { 10.5120/4699-6850 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:15.266048+05:30
%A K.Vedavathi
%A K.Srinivas Rao
%A A.VinayBabu
%T A Bivariate Autoregressive Software Reliability Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 7
%P 19-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software reliability models play a dominant role in the analysis of failure data for real time command and control software systems. Goel and Okumoto model is a non homogenous Poisson Process software reliability growth model which has gained a lot of importance in software reliability analysis and prediction. The process of parameter estimation is the major drawback of this model because the independent nature of attribute values is considered in estimation. But in real world applications, there are correlations existing among the attributes. Keeping this criterion, a bivariate autoregressive model of order 1 which forms a linear combination of variants namely software faults and test workers is proposed. A numerical illustration is presented to evaluate the performance of the developed model with that of the existing univariate autoregressive models and found that the proposed model outperforms than exiting model in evaluating and predicting software reliability.

References
  1. Jelinski,Z., and Moranda,P., 1972, Software Reliability Research, In Statistical Computer Performance Evaluation. 465-484.
  2. Yanyan Zheng, RenZuo Xu, 2008, A Composite Stochastic Process model for Software Reliability, International Conference on Computer Science and Software Engineering, 658 – 661.
  3. Denghua Mei, 2007, Novel Model for Software Reliability by Grey System Theory, 513-516.
  4. Khaled M.S. Faqih, 2009, What is Hampering the performance of Software Reliability Models? A Literature Review, Proceedings of the International Multi Conference of Engineers and Computer Scientists.
  5. Khalaf Khatatneh, 2009, Software Reliability Modeling using Soft Computing Technique, European Journal of Scientific research, 154-160.
  6. Raj Kiran,N., and Ravi,V., 2007, Software Reliability Prediction by Soft Computing Techniques, The Journal of Systems and Software.
  7. Yogesh Singh, Arvinder Kaur, Ruchika Malhotra, 2010, Prediction of fault prone Software modules using Statistical and Machine learning methods, International Journal of Computer Applications, 6-13.
  8. George E.P.Box, Gwilym M. Jenkins, and Gregpry C. Reinsel, 2003, Time Series Analysis, Forecasting and Control.
  9. Chen Zhongmin, Wu Yeqing, 2010, The application of theory and method of time series in the modeling of software reliability, Proceedings of International Conference on Information technology and Computer Science, 340-343.
  10. Jung-Hua Lo, 2010, Predicting Software Reliability with Support Vector Machines, International Conference on Computer Research and Development, 765-769.
  11. Yong Cao and Qing-Xin Zhu, 2010, The Software reliability forecasting Method using Fractals, Information technology Journal , 331-336.
  12. N.Raj Kiran, V.Ravi, 2007, Software reliability Prediction by Softcomputing techniques, The Journal of Systems and Software.
  13. Ryad Zemouri, Paul Ciprian Patic, 2010, Recurrent Radial Basis function Network for failure time series prediction, World academy of Science, Engineering and technology, 748 – 752.
  14. Sultan H. Aljahdali and Mohammed E.El-Telbany, 2008, Genetic Algorithms for Optimizing Ensemble of Models in Software reliability Prediction , ICGST-AIML Journal, 5-13.
  15. Liviu Adrian Cotfas and Andreea Diosteanu, 2010, Software Reliability in Semantic Web Service Composition Applications, Informatica Economica, Vol 14, No 4, 48 – 56.
Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability Autoregressive Model Reliability Evolution