  Good software cost prediction is important for effective project management
such as budgeting, project planning and control. In this paper, we present an
intelligent approach to software cost prediction. By integrating the
neuro-fuzzy technique with the well-accepted COCOMO model, our approach can
make the best use of both expert knowledge and historical project data. Its
major advantages include learning ability, good interpretability, and
robustness to imprecise and uncertain inputs. The validation using industry
project data shows that the model greatly improves prediction accuracy in
comparison with the COCOMO model.
