Analysis of Data Mining Techniques for Software Effort Estimation

Abstract

Software effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, datamining is used to improve an organization's software process quality, e.g. the accuracy of effort estimations. There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. In this study data preprocessing is implemented and effort is calculated using COCOMO Model. Then data mining techniques OLS Regression and K Means Clustering are implemented on preprocessed data and results obtained are compared and data mining techniques when implemented on preprocessed data proves to be more accurate then OLS Regression Technique.