Models of software projects input project details and output predictions via their internal tunings. The output predictions, therefore, are affected by variance in the project details P and variance in the internal tunings T. Local data is often used to constrain the internal tunings (reducing T). While constraining internal tunings with local data is always the preferred option, there exist some models for which constraining tuning is optional. We show empirically that, for the USC COCOMO family of models, the effects of P dominate the effects of T i.e. the output variance of these models can be controlled without using local data to constrain the tuning variance (in ten case studies, we show that the estimates generated by only constraining P are very similar to those produced by constraining T with historical data). We conclude that, if possible, models should be designed such that the effects of the project options dominate the effects of the tuning options. Such models can be used for the purposes of decision making without elaborate, tedious, and time-consuming data collection from the loca
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