Analyzing Influence of Outlier Elimination on Accuracy of Software Effort Estimation
Yeong‐Seok Seo, Kyung-A Yoon, Doo‐Hwan Bae · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2008
Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.