Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept

Abstract

In present day scenario, majority of software companies use object-oriented concept to develop software systems as it enables effective design, development, testing and maintenance, in addition to the optimal characterization of the software system. With the increase in number of these software systems, their effective maintenance aspect becomes very important day by day. In this study, Neuro-Fuzzy approach: hybrid neural network and fuzzy logic approach has been considered to develop a maintainability model using ten different object-oriented static source code metrics as input. This method is applied on maintainability data of two commercial software products such as UIMS and QUES. Rough set analysis (RSA) and principal component analysis (PCA) are used to select suitable set of metrics from the ten metrics employed to improve performance of maintainability prediction model. From experimental results, it is observed that Neuro-Fuzzy model can effectively predict the maintainability of object-oriented software systems. After implementing parallel computing concept, it is observed that the training time gets reduced to a significant amount when the number of computing nodes were increased. Further it is observed that selected subset of metrics using feature selection techniques i.e., PCA, and RSA was able to predict maintainability with higher accuracy.