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 i...

Full description

Bibliographic Details
Main Authors: G. Sudhakar, S. Nithiyanandam
Format: Article
Language:English
Published: Hindawi-IET 2023-02-01
Series:IET Software
Subjects:
Online Access:https://doi.org/10.1049/sfw2.12076
_version_ 1797425270881255424
author G. Sudhakar
S. Nithiyanandam
author_facet G. Sudhakar
S. Nithiyanandam
author_sort G. Sudhakar
collection DOAJ
description 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.
first_indexed 2024-03-09T08:13:38Z
format Article
id doaj.art-7bba20959b764bcbae072621310bcde8
institution Directory Open Access Journal
issn 1751-8806
1751-8814
language English
last_indexed 2024-03-09T08:13:38Z
publishDate 2023-02-01
publisher Hindawi-IET
record_format Article
series IET Software
spelling doaj.art-7bba20959b764bcbae072621310bcde82023-12-02T22:58:14ZengHindawi-IETIET Software1751-88061751-88142023-02-01171233610.1049/sfw2.12076DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifierG. Sudhakar0S. Nithiyanandam1Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College (Formerly Ranganathan Engineering College) Coimbatore Tamil Nadu IndiaDepartment of Mechanical Engineering Jai Shriram Engineering College Avinashipalayam Tirupur Tamil Nadu IndiaAbstract 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.https://doi.org/10.1049/sfw2.12076deterministic initialisation methoddistributed object‐orientedelastic net regulariserK‐means clusteringneural networkparallel scalability class dependency graph
spellingShingle G. Sudhakar
S. Nithiyanandam
DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
IET Software
deterministic initialisation method
distributed object‐oriented
elastic net regulariser
K‐means clustering
neural network
parallel scalability class dependency graph
title DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
title_full DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
title_fullStr DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
title_full_unstemmed DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
title_short DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier
title_sort doosra distributed object oriented software restructuring approach using dim k means and mad based enrnn classifier
topic deterministic initialisation method
distributed object‐oriented
elastic net regulariser
K‐means clustering
neural network
parallel scalability class dependency graph
url https://doi.org/10.1049/sfw2.12076
work_keys_str_mv AT gsudhakar doosradistributedobjectorientedsoftwarerestructuringapproachusingdimkmeansandmadbasedenrnnclassifier
AT snithiyanandam doosradistributedobjectorientedsoftwarerestructuringapproachusingdimkmeansandmadbasedenrnnclassifier