  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.
