Predicting Object-Oriented Software Maintainability using Hybrid Neural Network with Parallel Computing Concept

Abstract

Software maintenance is an important aspect of software life cycle development, hence prior estimation of effort for maintainability plays a vital role. Existing approaches for maintainability estimation are mostly based on regression analysis and neural network approaches. It is observed that numerous software metrics are even used as input for estimation. In this study, Object-Oriented software metrics are considered to provide requisite input data for designing a model. It helps in estimating the maintainability of Object-Oriented software. Models for estimating maintainability are designed using the parallel computing concept of Neuro-Genetic algorithm (hybrid approach of neural network and genetic algorithm). This technique is employed to estimate the software maintainability of two case studies such as the User Interface System (UIMS), and Quality Evaluation System (QUES). This paper also focuses on the effectiveness of feature reduction techniques such as rough set analysis (RSA) and principal component analysis (PCA). The results show that, RSA and PCA obtained better results for UIMS and QUES respectively. Further, it observed the parallel computing concept is helpful in accelerating the training procedure of the neural network model.