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.
Index Terms—Effort Estimation, Neural Network, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model
Jaswinder Kaur is doing M.Tech. from department of Computer Science & Engineering & I.T. of Baba Banda Singh Bahadur Engineering College, Fateh Garh Sahib, Punjab, India.
Satwinder Singh is working as Lecturer in department of Computer Science & Engineering & I.T. of Baba Banda Singh Bahadur Engineering College, Fateh Garh Sahib, Punjab, India.
Dr. Karanjeet Singh Kahlon is working as Professor with the Computer Science & Engineering Department, Guru Nanak Dev University, Amritsar, Punjab, India.
Pourush Bassi is working as Lecturer in department of Computer Science & Engineering of Rayat Bahra Institute of Engineering & Bio-Technology, Sahauran, Mohali, Punjab, India
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.
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