Zhihao Chen
Tim Menzies
Abstract
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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Index Terms
- Feature subset selection can improve software cost estimation accuracy
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Feature subset selection can improve software cost estimation accuracy
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