Title: Better Software Analytics via "DUO": Data Mining Algorithms Using/Used-by Optimizers,
Abstract: 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 the 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.