Cost estimation is important in software development for controlling and planning software risks and schedule. Good estimation models, such as COCOMO, can avoid insuffcient resources being allocated to a project. In this study, we find that COCOMO’s estimated can be improved via WRAPPER- a feature subset selection method developed by the data mining community. Using data sets from the PROMISE repository, we show WRAPPER significantly and dramatically improves COCOMO’s predictive power.
This is a joint study between TTOY, USC, and University of Hawaii.
@inProceedings{chen05,
author= {Zhihao Chen and Tim Menzies and Dan Port},
title= {Feature Subet Selection Improves Software Cost Estimation}
booktitle= {Proceedings, PROMISE workshop, ICSE 2005, St. Louis},
year= {2005},
notes= {Available from \url{http://menzies.us/pdf/05fsscocomo.pdf}}
}