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, their 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