A Neuro-Fuzzy Based Software Reusability Evaluation System with Optimized Rule Selection

Abstract

There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. We critically analyzed the CK metrics, tried to remove the inconsistencies and devised neuro-fuzzy framework that gets input in form of tuned WMC, DIT, NOC, CBO, LCOM values of a software component and output can be obtained in terms of reusability. This paper also shows how a small number of fuzzy rules can be selected for designing initial fuzzy rule-base for neuro-fuzzy systems. It consists of two phases: generation of candidate rules by IDS decision tree algorithm and rule pruning by evaluation of rules with help of two rule evaluation criteria. The developed reusability evaluation system has produced high precision results. Hence, the developed system can be used for identification and extraction of OO based reusable components from legacy systems and evaluation of developed or developing reusable components