[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86362-en":3,"doc-seo-86362-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86362,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","LLM-Driven Cost-Effective Requirements Change Impact Analysis","Requirements change impact analysis is central to requirements engineering, but manual identification of affected requirements is error-prone and costly under tight budgets. The work proposes ProReFiCIA, an LLM-driven approach that automatically identifies impacted requirements when changes occur. An extensive evaluation using multiple LLMs and prompt variants shows 85.7% recall on an unseen industrial dataset. Only 3.0% of requirements need engineer review; adding domain knowledge via RAG raises recall to 95.8% with a small cost increase to 3.4%.","arXiv :2511 .00262v 3 [ cs . SE] 13 Jul 2026  \nLLM-Driven Cost-Effective Requirements Change Impact Analysis  \nROMINA ETEZADI, School of Electrical Engineering and Computer Science, University of Ottawa, Canada SALLAM ABUALHAIJA, SnT Centre for Security, Reliability, and Trust, University of Luxembourg, Luxembourg  \nCHETAN ARORA, Faculty of Information Technology, Monash University, Australia  \nLIONEL BRIAND, School of Electrical Engineering and Computer Science, University of Ottawa, Canada and Lero Centre, University of Limerick, Ireland  \nRequirements are inherently subject to changes throughout the software development lifecycle. Within the limited budget available to requirements engineers, manually identifying the impact of such changes on other requirements is both error-prone and effort-intensive. That might lead to overlooked impacted requirements, which, if not properly managed, can cause serious issues in the downstream tasks. Inspired by the growing potential of large language models (LLMs) across diverse domains, we propose ProReFiCIA, an LLM-driven approach to automatically identify impacted requirements when changes occur. We conduct an extensive evaluation of ProReFiCIA using several LLMs and prompts variants tailored to this task. Using the best combination of an LLM and a prompt variant, ProReFiCIA achieves 85.7% recall on an unseen industrial dataset, demonstrating its effectiveness in identifying impacted requirements. Further, the cost of applying ProReFiCIA remains small, as the engineer only needs to review the predicted impacted requirements, which represent 3.0% of the entire set of requirements. Lastly, incorporating domain knowledge into the model via RAG increases recall to 95.8% while slightly raising the cost to only 3.4% .  \nAdditional Key Words and Phrases: Change Impact Analysis, Requirement Engineering, Natural Language Processing (NLP), Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Cache-Augmented Generation (CAG) .  \nACM Reference Format:  \nRomina Etezadi, Sallam Abualhaija, Chetan Arora, and Lionel Briand. 2026. LLM-Driven Cost-Effective Requirements Change Impact Analysis . 1, 1 (July 2026), 34 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 Introduction  \nSoftware systems today are increasingly complex, distributed, and tightly coupled, making even seemingly minor changes potentially risky and costly. In this context, Change Impact Analysis (CIA) plays a critical role in Requirements Engineering (RE) by systematically identifying, evaluating, and managing the effects of proposed modifications [42] . The primary objective of CIA  \nAuthors’ Contact Information: Romina Etezadi, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada, [retez068@uottawa.ca](retez068@uottawa.ca); Sallam Abualhaija, SnT Centre for Security, Reliability, and Trust, University of Luxembourg, Luxembourg, Luxembourg, [sallam.abualhaija@uni.lu](sallam.abualhaija@uni.lu); Chetan Arora, Faculty of Information Technology, Monash University, Melbourne, Australia, [chetan.arora@monash.edu](chetan.arora@monash.edu); Lionel Briand, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada and Lero Centre, University of Limerick, Limerick, Ireland, [lbriand@uottawa.ca](lbriand@uottawa.ca).  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires [prior specific permission and/or a fee. Request permissions from permissions@acm.org](pr","cbCaiutGy32t3v05","https://ap.wps.com/l/cbCaiutGy32t3v05","pdf",1487117,1,34,"English","en",105,"# Introduction\n## Problem Context and Motivation\n## Traditional and Automated Approaches","[{\"question\":\"What challenge does the document address in change impact analysis for requirements?\",\"answer\":\"It addresses how manually identifying which other requirements are affected by requirement changes is error-prone and effort-intensive, often causing impacted requirements to be overlooked.\"},{\"question\":\"How does ProReFiCIA work and what does it aim to do?\",\"answer\":\"ProReFiCIA is an LLM-driven approach that automatically identifies impacted requirements when changes occur, reducing the engineer’s effort to a targeted review of predicted items.\"},{\"question\":\"What performance and cost results does the evaluation report?\",\"answer\":\"Using the best LLM and prompt variant, ProReFiCIA reaches 85.7% recall on an unseen industrial dataset, with engineers reviewing only 3.0% of all requirements. With domain knowledge added via RAG, recall improves to 95.8% while review-related cost rises slightly to 3.4%.\"}]",1784210946,86,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"llm-driven-cost-effective-requirements-change-impact-analysis","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/llm-driven-cost-effective-requirements-change-impact-analysis/86362/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What challenge does the document address in change impact analysis for requirements?","Question",{"text":75,"@type":76},"It addresses how manually identifying which other requirements are affected by requirement changes is error-prone and effort-intensive, often causing impacted requirements to be overlooked.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ProReFiCIA work and what does it aim to do?",{"text":80,"@type":76},"ProReFiCIA is an LLM-driven approach that automatically identifies impacted requirements when changes occur, reducing the engineer’s effort to a targeted review of predicted items.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and cost results does the evaluation report?",{"text":84,"@type":76},"Using the best LLM and prompt variant, ProReFiCIA reaches 85.7% recall on an unseen industrial dataset, with engineers reviewing only 3.0% of all requirements. 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