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Reseach Article

Article:Discovery of Reliable Software using GOM on Interval Domain Data

by Dr. R. Satya Prasad, D. Haritha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 5
Year of Publication: 2011
Authors: Dr. R. Satya Prasad, D. Haritha
10.5120/3898-5461

Dr. R. Satya Prasad, D. Haritha . Article:Discovery of Reliable Software using GOM on Interval Domain Data. International Journal of Computer Applications. 32, 5 ( October 2011), 7-12. DOI=10.5120/3898-5461

@article{ 10.5120/3898-5461,
author = { Dr. R. Satya Prasad, D. Haritha },
title = { Article:Discovery of Reliable Software using GOM on Interval Domain Data },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 5 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number5/3898-5461/ },
doi = { 10.5120/3898-5461 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:21.931177+05:30
%A Dr. R. Satya Prasad
%A D. Haritha
%T Article:Discovery of Reliable Software using GOM on Interval Domain Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 5
%P 7-12
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software reliability growth models using Non-Homogeneous Poisson Process(NHPP) with a mean value function –dependent on Linearly falling fault detection rate as proposed in the literature is considered. The well known Sequential Probability Ratio Test (SPRT) procedure of statistical science is adopted for the model in order to decide upon the reliability/ unreliability of developed software. The performance of the proposed model is demonstrated by using 6 Data Sets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

GOM Maximum Likelihood Estimation Decision lines Software testing Software failure data