Experimental Evaluation of Algorithmic Effort Estimation Models using Projects Clustering

One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of clustering approach for estimating required effort in software projects. The effort estimation is carried out through SWR (StepWise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification an‎d Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, clustering of projects is applied to the estimation process. As indicated by the results of this study, the combination of clustering method and algorithmic estimation techniques can improve the accuracy of estimates.