Application of Group Method of Data Handling model for software maintainability prediction using object oriented systems

Abstract

Object-oriented methodology has emerged as most prominent in software industry for application development. Maintenance phase begins once the product is delivered and by software maintainability we mean the ease with which existing software could be modified during maintenance phase. We can improve and control software maintainability if we can predict it in the early phases of software life cycle using design metrics. Predicting the maintainability of any software has become critical with the increasing importance of software maintenance. Many authors have practiced and proved theoretical validation followed by empirical evaluation using statistical and experimental techniques for evaluating the relevance of any given metrics suite using many models. In this paper, we have presented an empirical study to evaluate the effectiveness of novel technique called Group Method of Data Handling (GMDH) for the prediction of maintainability over other models. Although many metrics have been proposed in the literature, software design metrics suite proposed by Chidamber et al. and revised by Li et al. have been selected for this study. Two web-based customized softwares developed using C# Language have been used for empirical study. Source code of old and new versions for both applications were collected and analysed against modifications made in every class. The changes were counted in terms of number of lines added, deleted or modified in the classes belonging to new version with respect to the classes of old version. Finally values of metrics were combined with “change” in order to generate data points. Hence, in this study an attempt has been made to evaluate and examine the effectiveness of prediction models for the purpose of software maintainability using real life web based projects. Three models using Feed Forward 3-Layer Back Propagation Network (FF3LBPN), General Regression Neural Network (GRNN) and GMDH are developed and performance of GMDH is compared against two others i.e. FF3LBPN and GRNN. With the aid of this empirical analysis, we can safely suggest that software professionals can use OO metric suite to predict the maintainability of software using GMDH technique with least error and best precision in an object oriented paradigm.