Building Better Quality Predictors Using "ε-Dominance"
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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 ϵ-dominance states that if there exists some ϵ value below which it is useless or impossible to distinguish results, then it is superfluous to explore anything less than ϵ. We say that for "large ϵ problems", the results space of learning effectively contains just a few regions. If many learners are then applied to such large ϵ 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 ϵ 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.
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