JOURNAL ARTICLE
Components Reusability Optimization based on Re-Structure Monolithic Code.
Published In: Journal of Cybersecurity & Information Management, 2026, v. 17, n. 1. P. 81 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Saeed, Zeyd; Ismael Khudair, Mustafa; Khader Ali Ibrahim, Ahmed; Nahi Abid, Rahman 3 of 3
Abstract
In modern software engineering, monolithic code structures are increasingly incompatible with the flexibility demanded by today’s platforms. These tightly coupled systems pose challenges for scalability, integration, and secure deployment. This paper presents a method for restructuring monolithic Java classes into optimized, reusable software components. We analyze each class using 19 object-oriented metrics from the CKJM suite, evaluating cohesion and coupling properties. Using our proposed framework—Good Global Optimization Dynamic Weighted Metrics (GGODWM)—we cluster interrelated classes and transform them into high-level components suitable for microservice environments. These components are evaluated within a Component Base Redesign Structure (CBRS) environment to measure reusability. Our experimental results show a 52% improvement in cohesion and coupling balance, outperforming traditional Turbo_MQ_based metrics. By enhancing component modularity and reducing interdependencies, the proposed approach contributes to more secure and maintainable code, thus supporting cybersecurity goals such as reduced attack surface and easier vulnerability management. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Cybersecurity & Information Management. 2026/01, Vol. 17, Issue 1, p81
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2026
- ISSN:27697851
- DOI:10.54216/jcim.170108
- Accession Number:187630776
- Copyright Statement:Copyright of Journal of Cybersecurity & Information Management is the property of American Scientific Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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