In order to use using advanced automated SE tools in an industrial
context, a business case must be made that these tools are valuable
for reducing defects and/or development effort and/or threats to
the project.  Making such a business case is a complicated by the
"local tuning" problem.  Normally, predictors for software effort
and defects and  and threat use local data to tune their predictions.
Such local tuning data is often unavailable or incomplete.

This paper shows that, when assessing the relative merits of
diffferent SE methods, a precise local tuning is not required.
STAR1 is a simulater annearling with a Bayesian post-processor that
explores the space of possible
local tunings within software process models.
STAR1 ranks project decisions in terms of their effects on  effort
and defects and threats.  When applied to NASA case studies, STAR1
found that a very clear business case for advanced automated SE
tools; i,e, in those case studies, advanced ASE tools were were
essential for  minimizing effort, defects, and project threats.
