An unsupervised learning and fuzzy logic approach for software category identification and capacity planning

Abstract

A hybrid unsupervised neural network and fuzzy logic approach is presented to achieve the primary goal of software categorization and feature interpretation. This method permits new software applications to be evaluated quickly for capacity planning and project management purposes. Fuzzy logic techniques were successfully applied to interpret the internal structure of the trained network, leading to an understanding of which application attributes most clearly distinguish the resulting categories. The resulting fuzzy membership functions can be used as inputs to subsequent analysis. These techniques can derive useful categories based on broad, external attributes of the software. This makes the technique useful to users of off-the-shelf software or to developers in the early stages of program specification. Experiments explicitly demonstrated the advantages of this method.<>