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This work designs, develops, and empirically validates a hybrid NLP framework called the Agile Requirement Quality (ARQ) framework to automatically detect requirement defects. Mixed-methods Design Science Research is used to build a hybrid BERT + heuristic artifact and validate it via a 5,000-story gold-standard corpus and a quasi-experiment with Agile teams.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/natural-language-processing-for-automated-requirement-engineering-in-agile-software-development-article/241343/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/natural-language-processing-for-automated-requirement-engineering-in-agile-software-development-article/241343.png","ImageObject",442,249,{"name":42,"@type":43},"wps_ap_test_251126_0180","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-11","2026-09-12",true,{"@type":52,"interactionType":53,"userInteractionCount":26},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Why does manual requirement engineering become a bottleneck in Agile development?","Question",{"text":62,"@type":63},"Manual processing of large-scale natural-language user stories creates cognitive overload and inconsistency. Backlogs become ambiguous, duplicated, and incomplete, leading to downstream defects and project risk.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What is the ARQ framework proposed in the study?",{"text":67,"@type":63},"ARQ is a hybrid Natural Language Processing framework that combines BERT with heuristic rules. It automates detection of common requirement defects during backlog refinement.",{"name":69,"@type":60,"acceptedAnswer":70},"How was the ARQ framework validated and what were the results?",{"text":71,"@type":63},"The framework was validated in-vitro against a 5,000-story annotated corpus and in-situ via a quasi-experiment with professional Agile teams. Results show high efficacy, including higher defect detection and reduced defect leakage with strong usability measured by SUS.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},241343,1791721233,{"code":4,"msg":81,"data":82},"success",[83,87,92,97,102,107,112,117,122],{"id":84,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":85,"slug":86},11,90,"presentations",{"id":88,"doc_module":22,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},12,"Resumes",80,"resumes",{"id":93,"doc_module":22,"doc_module_name":25,"category_name":94,"show_sort_weight":95,"slug":96},14,"Invoices",70,"invoices",{"id":98,"doc_module":22,"doc_module_name":25,"category_name":99,"show_sort_weight":100,"slug":101},15,"Posters",60,"posters",{"id":103,"doc_module":22,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},16,"Social Media",50,"social-media",{"id":108,"doc_module":22,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},17,"Forms",40,"forms",{"id":113,"doc_module":22,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},18,"Letters",30,"letters",{"id":118,"doc_module":22,"doc_module_name":25,"category_name":119,"show_sort_weight":120,"slug":121},21,"Paper Templates",5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":81,"data":127},{"doc_id":78,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":84,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":135,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":93,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":120},8796095027276,"https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=","NATURAL LANGUAGE PROCESSING FOR AUTOMATED REQUIREMENT ENGINEERING IN AGILE SOFTWARE DEVELOPMENT  \nMuchamad Sobri Sungkar 1, Serikbek Baibek2, and Salma Hamdan3  \n1 Universitas Harkat Negeri, Indonesia  \n2 Kazakh National Technical University, Kazakhstan  \n3 Al al-Bayt University, Jordan  \n\n| Corresponding Author:\u003Cbr>Muchamad Sobri Sungkar,\u003Cbr>Electronic Engineering Study Program, Harkat Negeri University.\u003Cbr>44M5+766 eks-Poltek Harber, Jl. Mataram No.9, Pesurungan Lor, Kec. Margadana, Kota Tegal, Jawa Tengah 52147, Indonesia\u003Cbr>Email: [sobrisungkar@gmail.com](sobrisungkar@gmail.com) |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Received: June 2, 2025\u003Cbr>Revised: September 15, 2025\u003Cbr>Accepted: November 10, 2025\u003Cbr>Online Version: December 16, 2025 |  | Abstract\u003Cbr>Manual Requirement Engineering (RE) in Agile software development creates a significant bottleneck. The reliance on natural language user stories at scale results in high-volume backlogs prone to ambiguity, duplication, and incompleteness, leading to costly, downstream development defects. This research aims to design, develop, and empirically validate a novel, hybrid Natural Language Processing (NLP) framework, termed the Agile Requirement Quality (ARQ) framework, to automate the detection of these common requirement defects. The goal is to reduce cognitive load and improve defect detection velocity during backlog refinement. A mixed-methods Design Science Research (DSR) methodology was employed. We developed the ARQ artifact (a hybrid BERT and heuristic model) and validated it both in-vitro against a 5,000-story “gold standard” annotated corpus (Fleiss ’ Kappa 0.86) and in-situ through a quasi-experiment with professional Agile teams. The findings demonstrate high efficacy. In-vitro validation achieved high accuracy (overall 95.2%, with F1-scores of 0.87 for ambiguity and 0.94 for duplication) . The in-situ experiment was conclusive: the ARQ-assisted team achieved a 73% increase in defect detection and an 87.5% reduction in “defect leakage” compared to the control team, registering high usability (88.5 SUS) . This study provides robust empirical evidence that NLP-driven automation is a viable, high-impact strategy for mitigating risk in Agile RE. The framework functions as a practical “augmented intelligence” tool, significantly reducing defect leakage and improving quality assurance velocity.\u003Cbr>Keywords: requirement engineering, agile software development, natural language processing (nlp), quality assurance, design science research\u003Cbr>\u003Cbr>© 2025 by the author(s)\u003Cbr>This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY SA) license ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)). |\n| Journal Homepage How to cite: | [https://research.adra.ac.id/index.php/jcsa](https://research.adra.ac.id/index.php/jcsa)\u003Cbr>Sungkar, M. S., Baibek, S., & Hamdan, S. (2025) . Natural Language Processing for Automated Requirement Engineering in Agile Software Development. Journal of\u003Cbr>Computer Science Advancements, 3(6), 330–343.\u003Cbr>[https://doi.org/10.70177/jsca.v3i6.2646](https://doi.org/10.70177/jsca.v3i6.2646) |  |\n| Published by: | Yayasan Adra Karima Hubbi |  |\n\nINTRODUCTION  \nAgile methodologies have become the dominant paradigm for modern software development, fundamentally altering the landscape of project management and software delivery (Chou, 2024) . Principles articulated in the Agile Manifesto, prioritizing individual interactions, working software, customer collaboration, and responding to change, have been widely adopted across the industry. Methodologies such as Scrum, Kanban, and Extreme Programming (XP) provide frameworks that enable teams to deliver value to stakeholders with high velocity and flexibility (Eramo et al., 2024) . This iterative and incremental approach stands in stark contrast to traditional “waterfal","cbCaihbKlgizal8q","https://ap.wps.com/l/cbCaihbKlgizal8q","pdf",297443,9,"English","# Introduction\n## Agile and Requirement Engineering\n## User Stories and Product Backlogs\n## Bottlenecks in Manual Requirement Analysis","[{\"question\":\"Why does manual requirement engineering become a bottleneck in Agile development?\",\"answer\":\"Manual processing of large-scale natural-language user stories creates cognitive overload and inconsistency. Backlogs become ambiguous, duplicated, and incomplete, leading to downstream defects and project risk.\"},{\"question\":\"What is the ARQ framework proposed in the study?\",\"answer\":\"ARQ is a hybrid Natural Language Processing framework that combines BERT with heuristic rules. It automates detection of common requirement defects during backlog refinement.\"},{\"question\":\"How was the ARQ framework validated and what were the results?\",\"answer\":\"The framework was validated in-vitro against a 5,000-story annotated corpus and in-situ via a quasi-experiment with professional Agile teams. Results show high efficacy, including higher defect detection and reduced defect leakage with strong usability measured by SUS.\"}]","Natural Language Processing for Automated Requirement Engineering in Agile Software Development - Article | PDF",1789173910]