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Finding the Right Data for Software Cost Modeling
November/December 2005 (vol. 22 no. 6)
pp. 38-46
Zhihao Chen, University of Southern California
Barry Boehm, University of Southern California
Tim Menzies, Portland State University
Daniel Port, University of Hawaii
Strange to say, when building a software cost model, sometimes it's useful to ignore much of the available cost data. One way to do this is to perform data-pruning experiments after data collection and before model building. Experiments involving a set of Unix scripts that employ a variable-subtraction algorithm from the WEKA (Waikato Environment for Knowledge Analysis) data-mining toolkit illustrate this approach's effectiveness.

This article is part of a special issue on predictor modeling.

Index Terms:
software engineering, time estimation, cost modeling, COCOMO, feature subset selection, wrapper
Zhihao Chen, Barry Boehm, Tim Menzies, Daniel Port, "Finding the Right Data for Software Cost Modeling," IEEE Software, vol. 22, no. 6, pp. 38-46, Nov.-Dec. 2005, doi:10.1109/MS.2005.151
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