Stable rankings for different effort models

There exists a large and growing number of proposed estimation methods but little conclusive evidence ranking one method over another. Prior effort estimation studies suffered from "conclusion instability", where the rankings offered to different methods were not stable across (a) different evaluation criteria; (b) different data sources; or (c) different random selections of that data. This paper reports a study of 158 effort estimation methods on data sets based on COCOMO features. Four "best" methods were detected that were consistently better than the "rest" of the other 154 methods. These rankings of "best" and "rest" methods were stable across (a) three different evaluation criteria applied to (b) multiple data sets from two different sources that were (c) divided into hundreds of randomly selected subsets using four different random seeds. Hence, while there exists no single universal "best" effort estimation method, t...

• Type: Journal
• Year: 2010
• Where: ASE
• Authors: Tim Menzies, Omid Jalali, Jairus Hihn, Daniel Baker, Karen T. Lum
• Keywords: ASE 2010, Effort Estimation, Effort Estimation Method, Estimation Method