A Generic Data Mining Model for Software Cost Estimation Based on Novel Input Selection Procedure

摘要

It is always preferable for any estimation model to be inclusive as accuracy in estimation models inherently lie with their inclusiveness. Software cost estimation is the prediction of development effort and time required to develop a software project and being predictive in nature, it demands for inclusiveness, which will accordingly bring the accuracy in it. In this study, a generic model for software cost estimation using an input selection procedure is proposed. The proposed model brings inclusiveness into the already available data mining techniques of software cost estimation by sensitively choosing a subset of highly relevant project attributes and ignoring the less relevant ones. In this article, a diverse set of data mining techniques for software cost estimation are considered. All these techniques are experimented on five data sets before and after passed through the proposed procedure. The obtained results showed that newly generated techniques after being passed through the proposed procedure offer accurate results up in the way of efficiency in software cost estimation.