  Software project management makes extensive use of predictive modeling to
estimate product size, defect proneness and development effort. Although
uncertainty is acknowledged in these tasks, fuzzy inference systems, designed
to cope well with uncertainty, have received only limited attention in the
software engineering domain. In this study we empirically investigate the
impact of two choices on the predictive accuracy of generated fuzzy inference
systems when applied to a software engineering data set: sampling of
observations for training and testing; and the size of the rule set generated
using fuzzy c-means clustering. Over ten samples we found no consistent pattern
of predictive performance given certain rule set size. We did find, however,
that a rule set compiled from multiple samples generally resulted in more
accurate predictions than single sample rule sets. More generally, the results
provide further evidence of the sensitivity of empirical analysis outcomes to
specific model-building decisions.
