Bayesian Regularization based Neural Network Tool for Software Effort Estimation

Bayesian Regularization based Neural Network Tool for Software Effort Estimation

Harwinder kaur

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Dalwinder Singh Salaria

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Lovely Professional University

Bayesian Regularization based Neural Network Tool for Software Effort Estimation

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Abstract

Rapid growth of software industry leads to need of new technologies. Software effort estimation is one of the areas that need more concentration. Exact estimation is always a challenging task. Effort Estimation techniques are broadly classified into algorithmic and non-algorithmic techniques. An algorithmic model provides a mathematical equation for estimation which is based upon the analysis of data gathered from previously developed projects and Non-algorithmic techniques are based on new approaches, such as Soft Computing Techniques. Effective handling of cost is a basic need for any Software Organization. The main tasks for Software development estimation are determining the effort, cost and schedule of developing the project under consideration. Underestimation of project done knowingly just to win contract results into loses and also the poor quality project. So, accurate cost estimation leads to effective control of time and budget during software development. This paper presents the performance analysis of different training algorithms of neural network in effort estimation. For sake of ease, we have developed a tool in MATLAB and at last proved that Bayesian Regularization [20] gives more accurate results than other training algorithms.

References

19 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Harwinder kaur. 2013. \u201cBayesian Regularization based Neural Network Tool for Software Effort Estimation\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 13 (GJCST Volume 13 Issue D2).

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GJCST Volume 13 Issue D2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

back propagation bayesian regularization (trainbr)effort estimation gradient descent (traingdx)levenberg-marquardt (trainlm)Matlab

Submission ReceivedApril 14, 2013
Peer Review Double Blind
Handling Editor
Accepted May 5, 2013
Published May 15, 2013

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v1.2

Issue date

May 19, 2013

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - D: Neural & AI

Authors: Harwinder Kaur, Dalwinder Singh Salaria (PhD/Dr. count: 0)

View Count (all-time): 298

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Publish Date: 2013 05, Sun

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Total Views: 9666

Total Downloads: 2475

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