Software development effort prediction is one of the most critical activities in managing software projects. Algorithmic effort prediction models, which have dominated the software engineering community, are limited by their inability to cope with uncertainties and imprecision surrounding software projects early in the development life cycle. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. There are evidences that soft computing has been able to address some of the problems associated with previous models. However, there is no common ground for assessing and comparing these soft computing based prediction techniques. This thesis presents an evaluation scheme for soft computing based effort prediction techniques. We present a critical survey of the state-of-the-art application of soft computing in development effort prediction. Based on the survey results, we propose and implement a transparent and adaptive fuzzy logic framework for effort prediction.