Publication:
Accurate Estimates Without Calibration?

dc.contributor.authorMenzies, Tim
dc.contributor.authorWilliams, Steve
dc.contributor.authorElrawas, Oussama
dc.contributor.authorBaker, Daniel
dc.contributor.authorBoehm, Barry
dc.contributor.authorHihn, Jairus
dc.contributor.authorLum, Karen
dc.contributor.authorMadachy, Ray
dc.date.accessioned2015-08-12T18:50:39Z
dc.date.available2015-08-12T18:50:39Z
dc.date.issued2008
dc.description.abstractMost 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.en_US
dc.identifier.urihttps://hdl.handle.net/10945/46032
dc.rightsThis 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.en_US
dc.titleAccurate Estimates Without Calibration?en_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Madachy_Accurate.2008.pdf
Size:
270.81 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.35 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections