Parameter Estimation of Software Reliability Growth Model

Authors

  • Bisma Gulzar. Mir
  • Sheikh Riyaz-ul-Haq

Abstract

Reliability normally covers each and every part of the device thinking about hardware, software program and processes but software program reliability paperwork an important factor as we are continually worried with efficient and quality software program. Software reliability is a crucial characteristic of software, nice for predicting the diploma of creditability of the precise software for the specified time frame and in particular surroundings. Software reliability models are categorized into two; one is the static version in which modeling and evaluation of software logic is performed at the same code, different one is dynamic model that observes the brief behavior of debugging technique in the course of checking out section. But the model parameters are in nonlinear relationships which create many troubles in locating parameters which might be best using traditional techniques like maximum likelihood and least rectangular estimation. Various algorithms have been introduced which makes the task of parameter estimation less complicated. Parameter estimation of Non-homogenous Poisson Procedure (NHPP) fashions, the use of MLE (Maximum likelihood Estimation) and seek algorithms referred to as particle swarm optimization has been explored in this paper. The incorporation of different device learning procedures within the prediction of software program reliability has proven enhancements in comparison to the statistical strategies.

Keywords: Software reliability, Support vector machine Software reliability growth models, Maximum likelihood Estimation, Particle Swarm Intelligence, parameter estimation

Cite this Article Bisma Gulzar Mir, Sheikh Riyaz-ul-Haq, Parameter Estimation of Software Reliability Growth Model. Journal of Artificial Intelligence Research & Advances. 2020; 7(1): 15–22p.

Downloads

Published

2020-05-20

Issue

Vol. 7 No. 1 (2020)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.