  Accurate software development effort estimation is critical to the success of
software projects. Although many techniques and algorithmic models have been
developed and implemented by practitioners, accurate software development
effort prediction is still a challenging endeavor in the field of software
engineering, especially in handling uncertain and imprecise inputs and
collinear characteristics. In this paper, a hybrid in-telligent model combining
a neural network model integrated with fuzzy model (neuro-fuzzy model) has been
used to improve the accuracy of estimating software cost. The performance of
the proposed model is assessed by designing and conducting evaluation with
published project and industrial data. Results have shown that the proposed
model demonstrates the ability of improving the estimation accuracy by 18%
based on the Mean Magnitude of Relative Error (MMRE) criterion.
