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Quasi-optimal Case-selective Neural Network Model for Software Effort Estimation by Eung Sup Jun; Jae Kyu Lee is a Computer Science article available to read on EtoBox.

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A number of software effort estimations have attempted using statistical models, case based reasoning, and neural networks. The research results showed that the neural network models perform at least as well as the other approaches, 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 dif®cult to maintain the performance of estimation models for the new breed of projects. Therefore, we propose a search method that ®nds the right level of relevant 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 input factors with the same values. Since there exist a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To ®nd the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the case-set selection algorithm. We have shown that the resulting model signi®cantly outperforms the original full model for the sof

Who reads Quasi-optimal Case-selective Neural Network Model for Software Effort Estimation?

Author

Eung Sup Jun; Jae Kyu Lee

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0957-4174)

Published

2001

Language

EN

Field

Computer Science (Physical Sciences)