A Comparative Analysis on Code Smell Refactoring Sequencing for Object-Oriented Systems using Hybrid Optimization Approaches

A Comparative Analysis on Code Smell Refactoring Sequencing for Object-Oriented Systems using Hybrid Optimization Approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A Comparative Analysis on Code Smell Refactoring Sequencing for Object-Oriented Systems using Hybrid Optimization Approaches Ritika, Dr.Navdeep Kaur, Dr.Amandeep Kaur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6570475/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Code smells are indicators of potential design flaws in object-oriented systems that can lead to maintenance challenges, reduced performance, and increased technical debt. Refactoring these smells is essential to improving software quality. However, the process of sequencing refactorings efficiently remains a complex optimization problem. This systematic review explores the role of hybrid optimization approaches in automating and enhancing code smell refactoring sequences. We analyse existing research on refactoring strategies, highlighting how heuristic, metaheuristic, and machine learning-based techniques have been combined to optimize refactoring decisions. Various hybrid models such as genetic algorithms, particle swarm optimization, ant colony optimization, and deep learning have been proposed to balance code maintainability, modularity, and performance. Our study categorizes these methods based on their effectiveness in detecting and mitigating different types of code smells, including long methods, large classes, and feature envy. We also discuss empirical evaluations that compare different hybrid approaches, shedding light on their strengths and limitations. This review provides a comprehensive synthesis of recent advancements in code smell refactoring sequencing and identifies future research directions. Software Engineering Optimization Software Engineering Code smells Refactoring Sequencing Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6570475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":450568058,"identity":"e2ad5755-fb96-4720-b5af-eaf7d0d48874","order_by":0,"name":"Ritika","email":"","orcid":"","institution":"Sri Guru Granth Sahib World University Fatehgarh Sahib","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Ritika","suffix":""},{"id":450568059,"identity":"85e01990-919b-4414-b8fa-ab496ef3c3f4","order_by":1,"name":"Dr.Navdeep 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