Data Challenges in AI Systems and their Solutions: A Requirements and AI Engineering Systematic Literature Review and Comparison

Data Challenges in AI Systems and their Solutions: A Requirements and AI Engineering Systematic Literature Review and Comparison | 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 Data Challenges in AI Systems and their Solutions: A Requirements and AI Engineering Systematic Literature Review and Comparison Yi Peng, Hans-Martin Heyn, Jennifer Horkoff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8077403/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The performance and reliability of AI-based software systems depend heavily on data, but managing this data throughout the system lifecycle presents significant challenges. Since data characteristics fundamentally define AI system behavior, specifying and managing these data characteristics should be a critical part of the system's requirements. Requirements Engineering (RE) is therefore essential. While both RE and AI Engineering communities address data-related issues for AI systems, their approaches and the gaps between them remain unclear. To investigate this, we conducted a systematic literature review of 227 primary studies to map data-related challenges for AI-based systems and compare the solutions emerging from both communities. We present two primary contributions: (1) a taxonomy of 28 data challenges for AI systems, addressed by the RE and AI Engineering communities, and structured around a data-centric lifecycle; and (2) a mapping of 108 existing solutions (50 from RE and 58 from other AI Engineering disciplines) to these challenges. This provides an overview of challenges and solutions from both perspectives, highlighting problems that are often overlooked, such as unused ''dark data'' and data selection explainability, while also showing challenges that are often the focus of existing work, such as ''lack of domain knowledge'' and ''data quality concerns''. Based on these findings, we outline a research agenda and discuss potential synergy between RE and AI Engineering. Practitioners and researchers can use these results to direct their future efforts in addressing data challenges in AI-based systems, leading to the development of more reliable, robust and trustworthy AI systems. Data Requirements Engineering AI Engineering Systematic Literature Review Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 14 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 21 Dec, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 14 Nov, 2025 First submitted to journal 10 Nov, 2025 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-8077403","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546279866,"identity":"b2af94e9-bafd-483f-a7f5-5efe278c5114","order_by":0,"name":"Yi Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDACZgglR7oWY9ItS2wgWqnBcQa2Bx931KbPj0hg/vCBKC2HGdgNZ545nrvxRgKb5AwitbBJ87Ydy904I4GNmYdoLX/bjqUbzkhg/vyHaC2MbTUJ8hIJDNLE6GCQPMzYbtjbdsBwA8/DNskeYrTwnT987MHPtjp5+fbkwx9+EKNF4QBjG5A6zGBwgLGBKHcxyDcwsAGpOhBjFIyCUTAKRgF2AABG+jK17jGBBwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Gothenburg","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Peng","suffix":""},{"id":546279869,"identity":"446828d9-e9dc-4618-89eb-c81d692fd1b7","order_by":1,"name":"Hans-Martin Heyn","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Hans-Martin","middleName":"","lastName":"Heyn","suffix":""},{"id":546279871,"identity":"b262dfe7-c34c-4dcf-90fe-6fa95ba32bd2","order_by":2,"name":"Jennifer Horkoff","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Horkoff","suffix":""}],"badges":[],"createdAt":"2025-11-10 13:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8077403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8077403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96591415,"identity":"d27a9532-da55-469c-a02d-658db8d813bb","added_by":"auto","created_at":"2025-11-24 06:32:12","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5383,"visible":true,"origin":"","legend":"","description":"","filename":"7081c0d05eb84b5597ee33a49eff46d0.json","url":"https://assets-eu.researchsquare.com/files/rs-8077403/v1/e625ec205b9a267c079cb630.json"},{"id":96605651,"identity":"3a86b333-70fa-409a-a24d-858be6b7433c","added_by":"auto","created_at":"2025-11-24 09:23:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2629556,"visible":true,"origin":"","legend":"","description":"","filename":"datarequirementsJournalversionnotitlepage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8077403/v1_covered_fb995596-053d-4eab-ae8e-7c7e9f19e237.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data Challenges in AI Systems and their Solutions: A Requirements and AI Engineering Systematic Literature Review and Comparison","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"requirements-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reen","sideBox":"Learn more about [Requirements Engineering](http://link.springer.com/journal/766)","snPcode":"766","submissionUrl":"https://submission.springernature.com/new-submission/766/3","title":"Requirements Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Data, Requirements Engineering, AI Engineering, Systematic Literature Review","lastPublishedDoi":"10.21203/rs.3.rs-8077403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8077403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nThe performance and reliability of AI-based software systems depend heavily on data, but managing this data throughout the system lifecycle presents significant challenges. 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