Publication:
Accurate Estimates Without Calibration?

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Authors
Menzies, Tim
Williams, Steve
Elrawas, Oussama
Baker, Daniel
Boehm, Barry
Hihn, Jairus
Lum, Karen
Madachy, Ray
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2008
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Abstract
Most process models calibrate their internal settings using local data. Collecting this data is expensive, tedious, and often an incomplete process. Is it possible to make accurate process decisions without historical data? Variability in model output arises from (a) uncertainty in model inputs and (b) uncertainty in the internal parameters that control the conversion of inputs to outputs. We find that, for USC family process models such as COCOMO and COQUALMO, we can control model outputs by using an AI search engine to adjust the controllable project choices without requiring local tuning. For example, in ten case studies, we show that the estimates generated in this manner are very similar to those produced by traditional methods (local calibration). Our conclusion is that, (a) while local tuning is always the preferred option, there exist some process models for which local tuning is optional; and (b) when building a process model, we should design it such that it is possible to use it without tuning.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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