CFP last date
20 December 2024
Reseach Article

A Statistical Process Control to Monitor the Software Quality

by D. Haritha, R. Satya Prasad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 11
Year of Publication: 2013
Authors: D. Haritha, R. Satya Prasad
10.5120/10128-4911

D. Haritha, R. Satya Prasad . A Statistical Process Control to Monitor the Software Quality. International Journal of Computer Applications. 62, 11 ( January 2013), 39-43. DOI=10.5120/10128-4911

@article{ 10.5120/10128-4911,
author = { D. Haritha, R. Satya Prasad },
title = { A Statistical Process Control to Monitor the Software Quality },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 11 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number11/10128-4911/ },
doi = { 10.5120/10128-4911 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:33.462802+05:30
%A D. Haritha
%A R. Satya Prasad
%T A Statistical Process Control to Monitor the Software Quality
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 11
%P 39-43
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software is the only man made Omni Present system contributing immensely providing complex and critical services to mankind. It's increasing popularity and usefulness enforces us to measure the software quality. In this paper Time domain failure data is applied to Statistical Process Control(SPC) method to monitor the quality of a software system. We propose a Statistical Control Method over the cumulative quantity between observations of time domain failure data using mean value function of an Non Homogeneous Poisson Process(NHPP) based Logarithmic Poisson Execution Time Model(LPETM). Maximum Likelihood estimation(MLE) is used to estimate the unknown parameters of proposed model. The SPC method employs LPETM to construct the control limits. Two failure data sets are used.

References
  1. J. D. Musa and K. Okumoto, "A logarithmic Poisson execution time model for software reliability measurement," in Proc. 7th Int. Conf. Software Eng. , 1984, pp. 230-238.
  2. Kazuhira Okumoto, "A Statistical Method for Software Quality Control," in IEEE Transactions on Software Engg. , vol. SE-11, No. 12, Dec 1985.
  3. M. Xie, T. N. Goh, P. Ranjan, "Some Effective control chart procedures for reliability monitoring", Elsevier, Reliability Engineering & System Safety 77(2002) 143-150.
  4. N. Boffoli, G. Bruno, D. Caivano, G. Mastelloni, "Statistical Process Control for oftware: a Systematic Approach", ESEM'08, October 9–10, 2008, Kaiserslautern, Germany, 327-329, Copyright 2008 ACM 978-1-59593-971-5/08/10.
  5. Shewhart, Walter A. , "The Economic Control of Quality of Manufactured Product", D. Van Nostrand Company, New York, 1931, reprinted by ASQC Quality Press, Milwaukee, Wisconsin, 1980.
  6. In Jae Myung ," Tutorial on maximum likelihood estimation", Journal of Mathematical Psychology 47 (2003) 90–100.
  7. A text book on "Software Reliability" By Hoang Pham.
  8. Qin Zhou, Yunzhao Luo, Zhaojun Wang, "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts" , Computational Statistics and Data Analysis 54 (2010) 1634_1645.
  9. Sunantha Teyarachakul , Suresh Chand , Jen Tang, "Estimating the limits for statistical process control charts:A direct method improving upon the bootstrap ", European Journal of Operational Research 178 (2007) 472–481.
Index Terms

Computer Science
Information Sciences

Keywords

Software Process Control Logarithmic Poisson Execution Time Model Control Charts