  This paper claims that a new field of empirical software engineering research
and practice is emerging: data mining using/used-by optimizers for empirical
studies or DUO. For example, data miners can generate models that are explored
by optimizers. Also, optimizers can advise how to best adjust the control
parameters of a data miner. This combined approach acts like an agent leaning
over the shoulder of an analyst that advises "ask this question next" or
"ignore that problem, it is not relevant to your goals". Further, those agents
can help us build "better" predictive models, where "better" can be either
greater predictive accuracy or faster modeling time (which, in turn, enables
the exploration of a wider range of options). We also caution that the era of
papers that just use data miners is coming to an end. Results obtained from an
unoptimized data miner can be quickly refuted, just by applying an optimizer to
produce a different (and better performing) model. Our conclusion, hence, is
that for software analytics it is possible, useful and necessary to combine
data mining and optimization using DUO.
