  Despite extensive research, many methods in software quality prediction still
exhibit some degree of uncertainty in their results. Rather than treating this
as a problem, this paper asks if this uncertainty is a resource that can
simplify software quality prediction.
  For example, Deb's principle of $\epsilon$-dominance states that if there
exists some $\epsilon$ value below which it is useless or impossible to
distinguish results, then it is superfluous to explore anything less than
$\epsilon$. We say that for "large $\epsilon$ problems", the results space of
learning effectively contains just a few regions. If many learners are then
applied to such large $\epsilon$ problems, they would exhibit a "many roads
lead to Rome" property; i.e., many different software quality prediction
methods would generate a small set of very similar results.
  This paper explores DART, an algorithm especially selected to succeed for
large $\epsilon$ software quality prediction problems. DART is remarkable
simple yet, on experimentation, it dramatically out-performs three sets of
state-of-the-art defect prediction methods.
  The success of DART for defect prediction begs the questions: how many other
domains in software quality predictors can also be radically simplified? This
will be a fruitful direction for future work.
