NDLI: Neural network approach for software cost estimation

Content Provider	IEEE Xplore Digital Library
Author	Tadayon, N.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. & Software Eng., Embry-Riddle Aeronaut. Univ., Daytona Beach, FL, USA (Tadayon, N.)
Abstract	Software engineering measurement and analysis specifically, cost estimation initiatives have been in the center of attention for many firms. In this paper, author explores the use of the expert judgment and machine learning techniques using neural network as well as referencing COCOMO II approach to predict the cost of software. Some primary work in the use of neural network in estimating software cost by [WITTIG & FINNIE] and Karunanithi (1992) produced very accurate results, but the major setback in their work was due to the fact that the accuracy of the result relied heavily on the size of the training set (1997). Understanding the adversity in applying neural networks, the author proposes a dynamic neural network that initially uses COCOMO II. The proposed network improves its estimation as the number of data set increases with input from expert judgment that affects the learning procedure.
Starting Page	815
Ending Page	818
File Size	133953
Page Count	4
File Format	PDF
ISBN	0769523153
DOI	10.1109/ITCC.2005.210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Costs Biological neural networks Artificial neural networks Software engineering Machine learning Machine learning algorithms Software tools Project management Computer networks
Content Type	Text
Resource Type	Article

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