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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
Citation:
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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