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Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP

by B. Vara Prasad Rao, K. Gangadhara Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 2
Year of Publication: 2016
Authors: B. Vara Prasad Rao, K. Gangadhara Rao
10.5120/ijca2016912030

B. Vara Prasad Rao, K. Gangadhara Rao . Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP. International Journal of Computer Applications. 154, 2 ( Nov 2016), 1-5. DOI=10.5120/ijca2016912030

@article{ 10.5120/ijca2016912030,
author = { B. Vara Prasad Rao, K. Gangadhara Rao },
title = { Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 2 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number2/26460-2016912030/ },
doi = { 10.5120/ijca2016912030 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:06.198015+05:30
%A B. Vara Prasad Rao
%A K. Gangadhara Rao
%T Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 2
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Statistical process control is a method of monitoring product in its development process using statistical techniques with the presumption that the products produced under identical process condition shall not always be alike with respect to some quality characteristic( s). However, if the observed variations are with in the tolerable limits statistical process control (SPC) methods would pass them for acceptance. This philosophy is adopted to decide the reliability and quality of a developed software by defining some quality measures and proposing a probability model for the quality measurements. The well known linear failure rate distribution( LFRD) is considered to propose a software reliability based on non-homogenous Poisson process (NHPP). Its mean value function is taken as a quality characteristic and SPC limits for it are developed. These control limits are exemplified to a live failure data to detect the out of control signals for the quality of the software based on the software failure data.

References
  1. Hoang Pham. A generalized logistic software reliability growth model. Operational Research Society of India(OPSEARCH), 42(4):322–331, Dec 2005.
  2. Huang and Kuo.S.Y. Analysis of incorporating logistic testing effort function into software reliability modeling. IEEE Transactions on Reliability, 51(3):261–270, Sept 2002.
  3. Huang.C.Y. Performance analysis of software reliability growth models with testing-effort and change-point. Journal of Systems and Software, 76(2), May 2005.
  4. Kantam.R.R.L. and Subba Rao.R. Pareto distribution - A software reliability growth model. International Journal of Performability Engineering, 5(3):275–281, 2009.
  5. Kantam.R.R.L., Priya.M.Ch., and Ravikumar.M.S. Moment type estimation in linear failure rate distribution. IAPQR Transactions, 39(1):87–97, 2014.
  6. Kim.H.C. Assessing software reliability based on NHPP using SPC. International Journal of Software Engineering and its Applications, 7(6):61–70, 2013.
  7. Lan.Y. and Leemis.L. The logistic exponential survival distribution. Naval Research Logistics, 55(3):252–264, 2007.
  8. Pham.H., Nordmann.L. and Zhang.X. A general imperfect software debugging model with s-shaped fault detection rate. IEEE Transaction on Reliability, (48):169–175, 1999.
  9. Yamada.S., Ohtera.H. and Narihisa.R. Software reliability growth models with testing effort. IEEE Transactions on Reliability, R-35:19–23, 1986.
  10. Quadri.S.M.K.,Peer.M.A., Ahmad.N. and Kumar.M. Non homogeneous poisson process software reliability growth model with generalized exponential testing effort function. RAU Journal of Research, 16(1-2):159–163, 2006.
  11. Pham.H. Software Reliability. Springer-Verlag, 2000.
  12. Vara Prasad Rao.B., Gangadhar Rao.K., and Srinivasa Rao.B. Inverse Rayleigh software reliability growth model. International Journal of Computer Applications, 75(6),2013.
  13. Pham.H. and Zhang.X. Non homogeneous poisson process software reliability and cost models with testing coverage. European Journal of Operational Research, (145):443–454, 2003.
  14. Srinivasa Rao.B., Vara Prasad Rao.B., and Kantam.R.R.L. Software reliability growth model based on half logistic distribution. Journal of Testing and Evalaution, 39(6),:1152–1157, 2011.
  15. Yamada.S., Tamura.Y. and Kimura.M. A software reliability growth model for a distributed development environment. Electronic and Communications in Japan, 83(3):1446–1453, 2003.
  16. Wood.A.P. Predicting software reliability. IEEE Computer, 11:69–77, 1996.
  17. Yamada.S. and Inoue.S. Testing coverage dependent software reliability growth modeling. International Journal of Quality, Reliability and Safety Engineering, 11(4):303–312, 2004.
  18. Yen.S.K., Huang.C.Y. and Michael.R.Lyu. An assesment of testing effort dependent software reliability growth models. IEEE Transactions on Reliability, 56(2):198–211, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

IRD MLE MSE NHPP SRGM

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