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Margaret K. Kulpa, Kent A. Johnson

"Interpreting the CMMI: A Process Improvement Approach, Second Edition"

The PPM used came within 5 percent of the total effort following the completion
of the requirements phase. We have also seen models built that successfully predict
number of delivered defects, mean time between failures, and number of system
failures during integration testing. The exciting thing about the use of the models is
that the focus of project management becomes trying to find defects and trying to
accurately measure performance, and not on dysfunctional interoffi ce politics.
Not to confuse the issue, but Table 19.3 is a simplification of a PPM. The basic
flaw in this model is that, by using the mean (or average) value of the historical
data points, we disregard the distribution between the upper and lower levels of
the phase. This means that if a project were to use this model to predict its effort
throughout the life cycle, the project would most probably never hit the actual
mean for each phase. Instead, the project effort would fall somewhere between the
range of upper and lower limits. This is a problem that can be corrected by incorporating
the upper and lower control limits into the model.


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