Quasi-optimal case-selective neural network model for software effort estimation

A number of software effort estimations have attempted using statistical mo dels, case based reasoning, and neural networks. The research results showe d that the neural network models perform at least as well as the other appr oaches, so we selected the neural network model as the estimator. However, since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies, it is very difficult to ma intain the performance of estimation models for the new breed of projects. Therefore, we propose a search method that finds the right level of relevan t cases for the neural network model. For the selected case set, the scale of the neural network model can be reduced by eliminating the qualitative i nput factors with the same values. Since there exist a multitude of combina tions of case sets, we need to search for the optimal reduced neural networ k model and corresponding case set. To find the quasi-optimal model from th e hierarchy of reduced neural network models, we adopted the beam search te chnique and devised the case-set selection algorithm. We have shown that th e resulting model significantly outperforms the original full model for the software effort estimation. This approach can be also used for building an y case-selective neural network. (C) 2001 Elsevier Science Ltd. All rights reserved.