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

The Impact of Sampling and Rule Set Size on Generated Fuzzy Inference System Predictive Accuracy: Analysis of a Software Engineering Data Set