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
