Paper Info
Title
Comparing Soft Computing Techniques For Early Stage Software Development Effort Estimations
Abstract
Accurately estimating the software size, cost, effort and schedule is probably the biggest challenge facing software developers today. It has major implications for the management of software development because both the overestimates and underestimates have direct impact for causing damage to software companies. Lot of models have been proposed over the years by various researchers for carrying out effort estimations. Also some of the studies for early stage effort estimations suggest the importance of early estimations. New paradigms offer alternatives to estimate the software development effort, in particular the Computational Intelligence (CI) that exploits mechanisms of interaction between humans and processes domain knowledge with the intention of building intelligent systems (IS). Among IS, Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques for software development effort estimation. In this paper neural network models and Mamdani FIS model have been used to predict the early stage effort estimations using the student dataset. It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in comparison to the neural network models based models.
Year
Venue
Field
2012
International Journal of Software Engineering & Applications
Data mining,Intelligent decision support system,Software,Artificial intelligence,Soft computing,Software sizing,Software development,Computational intelligence,Domain knowledge,Software development effort estimation,Engineering,Machine learning,Reliability engineering
DocType
Volume
ISSN
Journal
abs/1204.6396
International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.2, March 2012
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Roheet Bhatnagar104.39
Mrinal Kanti Ghose2122.75