Comparing Software Cost Prediction Models by a Visualization Tool

Abstract

A crucial issue in the software cost estimation area that has attracted the interest of software project managers is the selection of the best prediction method for estimating the cost of a project. Most of the prediction techniques estimate the cost from historical data. The selection of the best model is based on accuracy measures that are functions of the predictive error, whereas the significance of the differences can be evaluated through statistical procedures. However, statistical tests cannot be applied easily by non-experts while there are difficulties in the interpretation of their results. The purpose of this paper is to introduce the utilization of a visualization tool, the regression error characteristic curves in order to compare different prediction models easily, by a simple inspection of a graph. Moreover, these curves are adjusted to accuracy measures appeared in software cost estimation literature and the experimentation is based on two well-known datasets.