doi: 10.7763/IJCTE.2010.V2.109
Neural Network-A Novel Technique for Software Effort Estimation
- 1department of Computer Science & Engineering & I.T. of Baba Banda Singh Bahadur Engineering College, Fateh Garh Sahib, Punjab, India.
- 2Department, Guru Nanak Dev University, Amritsar, Punjab, India.
- 3department of Computer Science & Engineering of Rayat Bahra Institute of Engineering & Bio-Technology, Sahauran, Mohali, Punjab, India
Abstract
Estimating software 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 novel 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 for NASA software projects. A comparison between Artificial-Neural-Network Based Model (ANN) and Halstead, Walston-Felix, Bailey-Basili and Doty models were provided. 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 ANNs are effective in effort estimation.
Keywords
- Effort Estimation
- Neural Network
- Halstead Model
- Walston-Felix Model
- Bailey-Basili Model
- Doty Model
How to Cite
Jaswinder Kaur, Satwinder Singh, Karanjeet Singh Kahlon, and Pourush Bassi, "Neural Network-A Novel Technique for Software Effort Estimation," International Journal of Computer Theory and Engineering, vol. 2, no. 1, pp. 17-19, 2010. https://doi.org/10.7763/IJCTE.2010.V2.109
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).