Estimating test development effort is an important task in the management of large software projects. The task is challenging and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for neural model to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. In this paper, we explore the use of soft computing techniques to build a suitable model structure to utilize improved estimation of software effort; a comparison between neural network (NN) and neural fuzzy model; and the evaluation criteria are based upon MRE and MMRE. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. The results show that NF is effective in effort estimation.