DOOSRA—Distributed Object-Oriented Software Restructuring Approach using DIM-K-means and MAD-based ENRNN classifier

Abstract

There exists a need to generate well-designed software systems because of the extensive adoption of object-oriented programming in software growth. Thus, the total software maintenance cost is decreased and the component's reusability is augmented. However, the software system's internal structure worsens owing to extended maintenance activities. For enhancing the system's overall internal structure without varying its external behaviour, restructuring is an extensively utilised solution in this circumstance. Thus, utilising the Deterministic Initialisation Method based K-Means (DIM-K-Means) and Median Absolute Deviation-based Elastic Net Regulariser Neural Network (MAD-ENRNN) classifier, a framework called Distributed Object-Oriented (DOO) software restructuring model is created by the study. Five steps are undertaken by the developed framework. Centred on source code along with change history, the interactions amongst the classes are initially pre-processed where the dependencies of disparate classes are detected and formulated into a graphical structure. After that, from the graph, the extraction of significant features is done. Utilising a multi variant objective-based Aquila optimiser, the most pertinent features are selected as of the extracted features. Next, for minimising the complexity, the selected features are created into clusters. Then, the formed clusters are offered to the classifier named MAD-ENRNN. The DOO software is effectively restructured by MAD-ENRNN. The proposed methodology's performance is contrasted with the prevailing systems in an experimental evaluation. The outcomes displayed that the proposed framework is capable of restructuring the DOO software with improved accuracy of 9.94% when analogised to the top-notch methods.