AI-Assisted Test Scope Recommendation for Manual QA: A Framework and Evaluation

AI-Assisted Test Scope Recommendation for Manual QA: A Framework and Evaluation | 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 AI-Assisted Test Scope Recommendation for Manual QA: A Framework and Evaluation Arbaz Surti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9193663/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 Determining the scope of testing required for a software change is among the most consequential yet least structured decisions in manual quality assurance. In many enterprise teams, this decision relies on practitioner experience and informal negotiation, producing outcomes that vary with team composition rather than change complexity. This paper presents the AI-Assisted Test Scope Recommender (ATSR), a framework that applies large language models to generate structured test scope recommendations from feature specifications. ATSR produces multi-dimensional recommendations spanning five test type categories—functional, smoke, regression, integration, and exploratory— alongside a calibrated coverage depth (LOW, MEDIUM, or HIGH) based on change type classification, an indicative effort estimate, and actionable clarification flags identifying information gaps that would materially affect scope decisions. The framework was evaluated against 18 synthetic specifications derived from enterprise restaurant technology quality assurance practice, using a five-dimension rubric measuring Completeness, Specificity, Risk Calibration, Integration Awareness, and Clarification Flag Quality. ATSR achieved an overall score of 84.4% (380 out of 450 points), with near-perfect coverage depth accuracy (97.8%) and strong completeness (91.1%). To isolate the contribution of the framework design from the underlying model capability, a naive baseline using the same model and output schema with an unstructured prompt achieved 67.8% overall and correct depth calibration in only 33.3% of cases. An independent quality assurance practitioner scored ATSR outputs for six representative specifications at 91.3%, providing evidence that primary evaluator scores are conservative. The results demonstrate that structured prompting frameworks contribute materially and measurably over unstructured large language model querying for test scope planning. AI-assisted testing test scope recommendation large language models manual quality assurance coverage depth calibration prompt engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 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-9193663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618899646,"identity":"2dedad1b-27fb-4151-920b-eba6e97f9175","order_by":0,"name":"Arbaz Surti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RMQrCMBSA4YSuUddM9goRQfAsLhWhTnHuoCWTLh6goNQr6NK5pVCXqKvSpS46u2X0VUF0qNVNMP+QhpKPJAQhne4nI09zy4HBMMTHBAtL5gR/Q/D4/n0LarNNdFEoQKbHz9nRH3VqEyDKCQoJ3Q56lKIUsX2/IbrBmnsx7DaVafE2kjDKckJtDCThAoiBx8XElKSpLCCml5N5whdlhEnSoiEQtM+JGPJlGWnIqt0WLCVMnrBnJSFfAYne3aUuK/FBOWndnNjoooYu93dxlMGf4uvfj/d4nfg2hiXrX3K/WazT6XR/0hXPV1mMr6Dd0wAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Arbaz","middleName":"","lastName":"Surti","suffix":""}],"badges":[],"createdAt":"2026-03-22 21:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9193663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9193663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404365,"identity":"55cd6737-cf15-4c7d-9cef-62b1988707b6","added_by":"auto","created_at":"2026-04-08 09:15:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":433743,"visible":true,"origin":"","legend":"","description":"","filename":"AIAssistedTestScopeRecommendationAIAssistedTestScopeRecommendationforManualQAAFrameworkandEvaluation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9193663/v1_covered_9604c48f-dab1-4fc3-9647-4ce3eb0ab782.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Assisted Test Scope Recommendation for Manual QA: A Framework and Evaluation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"software-quality-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sqjo","sideBox":"Learn more about [Software Quality Journal](http://link.springer.com/journal/11219)","snPcode":"11219","submissionUrl":"https://submission.nature.com/new-submission/11219/3","title":"Software Quality Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AI-assisted testing, test scope recommendation, large language models, manual quality assurance, coverage depth calibration, prompt engineering","lastPublishedDoi":"10.21203/rs.3.rs-9193663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9193663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Determining the scope of testing required for a software change is among the most consequential yet least structured decisions in manual quality assurance. 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