Open AccessFeature subset selection can improve software cost estimation accuracy
Author(s) -
Zhihao Chen,
Tim Menzies,
Dan Port,
Barry Boehm
Publication year - 2005
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
ISBN - -159593-125-2
DOI - 10.1145/1083165.1083171
Subject(s) - cocomo , computer science , feature selection , data mining , software , selection (genetic algorithm) , schedule , estimation , feature (linguistics) , cost estimate , machine learning , software development , artificial intelligence , engineering , software construction , systems engineering , linguistics , philosophy , programming language , operating system
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.
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