Improving Credibility of Machine Learner Models in Software Engineering

Improving Credibility of Machine Learner Models in Software Engineering

Gary D. Boetticher (University of Houston – Clear Lake, USA)
Copyright: © 2007 |Pages: 21
DOI: 10.4018/978-1-59140-941-1.ch003
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Given a choice, software project managers frequently prefer traditional methods of making decisions rather than relying on empirical software engineering (empirical/machine learning- based models). One reason for this choice is the perceived lack of credibility associated with these models. To promote better empirical software engineering, a series of experiments are conducted on various NASA datasets to demonstrate the importance of assessing the ease/difficulty of a modeling situation. Each dataset is divided into three groups, a training set, and “nice/nasty” neighbor test sets. Using a nearest neighbor approach, “nice neighbors” align closest to same class training instances. “Nasty neighbors” align to the opposite class training instances. The “nice”, “nasty” experiments average 94% and 20%accuracy, respectively. Another set of experiments show how a ten-fold cross-validation is not sufficient in characterizing a dataset. Finally, a set of metric equations is proposed for improving the credibility assessment of empirical/machine learning models.

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