Can neural networks be easily interpreted in software cost estimation?

Software development effort estimation with the aid of neural networks has generally been viewed with skepticism by a majority of the software cost estimation community. Although, neural networks have shown their strengths in solving complex problems, the ir shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for cost estimation. In this paper, we study the interpretation of cost estimation models based on a Backpropagation three-layer Perceptron network. Our proposed idea comprises mainly of the use of a method that maps this neural network to a fuzzy rule based system. Consequently, if the obtained fuzzy rules are easily interpreted, the neural network will also be easy to interpret. Our case study is based on the COCOMO' 81 dataset.