  The effort invested in a software project is probably one of the most
important and most analyzed variables in recent years in the process of project
management. The limitation of algorithmic effort prediction models is their
inability to cope with uncertainties and imprecision surrounding software
projects at the early development stage. More recently attention has turned to
a variety of machine learning methods, and soft computing in particular to
predict software development effort. Soft computing is a consortium of
methodologies centering in fuzzy logic, artificial neural networks, and
evolutionary computation. It is important, to mention here, that these
methodologies are complementary and synergistic, rather than competitive. They
provide in one form or another flexible information processing capability for
handling real life ambiguous situations. These methodologies are currently used
for reliable and accurate estimate of software development effort, which has
always been a challenge for both the software industry and academia. The aim of
this study is to analyze soft computing techniques in the existing models and
to provide in depth review of software and project estimation techniques
existing in industry and literature based on the different test datasets along
with their strength and weaknesses
