Software effort estimation as a classification problem

Software cost estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Software cost estimation, up to now, has been treated as a regression problem. However, in order to prevent over/under estimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and classifies new software projects in one of the effort classes each corresponding to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find the corresponding effort classes. The proposed approach is applied to seven public data sets. Our experimental results show that hit rates obtained for effort estimation are around 90%-100%s. For point estimation, the results are also comparable to those in the literature.