[ACM Press the 2005 workshop - St. Louis, Missouri (2005.05.15-2005.05.15)] Proceedings of the 2005 workshop on Predictor models in software engineering - PROMISE '05 - Feature subset selection can improve software cost estimation accuracy

✍ Scribed by Chen, Zhihao; Menzies, Tim; Port, Dan; Boehm, Barry

• Book ID: 118059849
• Publisher: ACM Press
• Year: 2005
• Weight: 175 KB
• Volume: 0
• Category: Article
• ISBN-13: 9781595931252
• DOI: 10.1145/1083165.1083171

✦ Synopsis

Cost estimation is important in software development for controlling and planning software risks and schedule. Good estimation models, such as COCOMO, can avoid insufficient resources being allocated to a project. In this study, we find that COCOMO's estimates 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.