Class point approach for software effort estimation using soft computing techniques

The estimation of effort involved in developing a software product plays an important role in determining the success or failure of the product. Project managers require a reliable approach for software effort estimation. It is especially important during the early stage of the software development life cycle. An accurate software effort estimation is a major concern in current industries. In this paper, the main goal is to estimate the effort required to develop various software projects using class point approach. Then optimization of the effort parameters is achieved using adaptive regression based Multi-Layer Perceptron (ANN) technique to obtain better accuracy. Furthermore, a comparative analysis of software effort estimation using Multi-Layer Perceptron (ANN) and Radial Basis Function Network (RBFN) has been provided. By estimating the software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules.