Test Effort Estimation Based Upon Neural Fuzzy Model

Authors

  • Pradeep Kumar Bhatia
  • Vikas Chahar

Abstract

Abstract

Estimating test development effort is an important task in the management of large software projects. The task is challenging and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for neural model to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. In this paper, we explore the use of soft computing techniques to build a suitable model structure to utilize improved estimation of software effort; a comparison between neural network (NN) and neural fuzzy model; and the evaluation criteria are based upon MRE and MMRE. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. The results show that NF is effective in effort estimation.

Keywords: Effort estimation, neural network, fuzzy model, MRE, MMRE

Cite this Article

Vikas Chahar, Pradeep Kumar Bhatia. Test Effort Estimation Based Upon Neural Fuzzy Model. Journal of Artificial Intelligence Research & Advances. 2019; 6(1): 76–85p.


Downloads

Published

2019-05-15

Issue

Vol. 6 No. 1 (2019)

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.