AutoNetTest: A Platform-Aware Framework for Intelligent 5G Network Test Automation and Issue Diagnosis

AutoNetTest: A Platform-Aware Framework for Intelligent 5G Network Test Automation and Issue Diagnosis | 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 Research Article AutoNetTest: A Platform-Aware Framework for Intelligent 5G Network Test Automation and Issue Diagnosis Tongwei Tu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866913/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 The rapid evolution of 5G networks introduces intricate challenges in testing and diagnosis, necessitating effective solutions for robust operation. We introduce AutoNetTest, a groundbreaking framework that streamlines test automation through intelligent, platform-aware design. This framework integrates sophisticated machine learning methodologies, allowing for automatic detection of network issues and facilitating thorough diagnostics. With its modular architecture, AutoNetTest is capable of adapting to a variety of platforms, ensuring flexibility in diverse environments. Real-time data analytics play a crucial role in the system, enabling continuous monitoring of network performance and timely anomaly detection. By utilizing both supervised and unsupervised learning techniques, the framework efficiently classifies issues and offers actionable remediation strategies, underscoring a proactive approach to network quality management. Computer Architecture and Engineering Intelligent Test Automation Network Diagnosis Platforms Flexibility 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6866913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469502961,"identity":"627830eb-0b76-4f6b-9345-bc3c126aef02","order_by":0,"name":"Tongwei Tu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLCCBAYJOXmGwweATAkZgqp52MBaLIwNG48lgLTwEKeFgaEiseHwGQOwAEEt9vI9hg8eMEgwNrad+fzqRo0FDwP74aMb8NvCY2wA9AszO8/ZbdY5x4AO40lLu4FfC+82CaAWNsYZZ7cZ57ABtUjwmBHSsv1HAsjX9988M875R5yWbaBAlmA4cIb5cW4bMVqO5X+WSDCQMDBsOGbGnNsnAfQcAb+wNx9L/Pijoq5+PsPhx59zvtXJ8bMfPoZXCwSAY4SBTQJMElaOAMwfSFE9CkbBKBgFIwcAAHgWQg6+jz8fAAAAAElFTkSuQmCC","orcid":"","institution":"Embry-Riddle Aeronautical University","correspondingAuthor":true,"prefix":"","firstName":"Tongwei","middleName":"","lastName":"Tu","suffix":""}],"badges":[],"createdAt":"2025-06-11 01:51:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6866913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6866913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84434942,"identity":"8e6752c7-1cba-4de1-a5c9-0ce545f0708f","added_by":"auto","created_at":"2025-06-12 01:59:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":460867,"visible":true,"origin":"","legend":"","description":"","filename":"AutoNetTestAPlatformAwareFrameworkforIntelligent5GNetworkTestAutomationandIssueDiagnosis5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6866913/v1_covered_394e522e-3da3-4edd-ac84-77d3d6fea45d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAutoNetTest: A Platform-Aware Framework for Intelligent 5G Network Test Automation and Issue Diagnosis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Embry-Riddle Aeronautical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intelligent Test Automation, Network Diagnosis, Platforms Flexibility","lastPublishedDoi":"10.21203/rs.3.rs-6866913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6866913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid evolution of 5G networks introduces intricate challenges in testing and diagnosis, necessitating effective solutions for robust operation. 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