Comparison and evaluation of data mining techniques with algorithmic models in software cost estimation

Abstract

Software Cost Estimation (SCE) is one of important topics in producing software in recent decades. Real estimation requires cost and effort factors in producing software by using of algorithmic or Artificial Intelligent (AI) techniques. Boehm developed the Constructive Cost Model (COCOMO) that is one of the algorithmic SCE models. Also, these models contain three increasingly basic, intermediate and detailed forms, i.e. basic COCOMO is suitable for quick, early, rough order of among the estimates of required effort in producing software, but its accuracy is limited due to its loss of factors to account for difference between cost drivers. Intermediate COCOMO assumes these project attributes into account. In addition detailed COCOMO accounts for individual project phases used. The COCOMO algorithmic techniques families have used since 1981. In recent years, some techniques emerged by using intelligent techniques to solve and estimate the effort required in producing software. In this paper, different data mining techniques to estimate software costs are presented and then the results of each technique are evaluated and compared. However, NASA's projects to train and test each of these techniques are applied. Then, data set to train and test the data mining techniques improve the estimation accuracy of the models in many cases. We show the comparison between COCOMO model and data mining techniques here. The results indicate that these methods result in many benefit answers. Also we show the comparison of the estimation accuracy of COCOMO model with data mining techniques. Data mining techniques improve the estimation accuracy of the models in many cases. So the estimated effort more improvement in this models.