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This study evaluates whether large language models can draft accurate, coherent, and structured SRS documents. GPT-4 and CodeLlama are tested on a university club management system and compared to human benchmarks using eight criteria. Results show GPT-4 can match entry-level engineer quality and provide constructive feedback for rectification, while CodeLlama validation is less effective. Experiments across four use cases quantify time savings and improved productivity.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/using-llms-in-software-requirements-specifications-an-empirical-evaluation/240853/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/using-llms-in-software-requirements-specifications-an-empirical-evaluation/240853.png","ImageObject",442,249,{"name":88,"@type":89},"Mimi","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-11-04","2026-09-11",true,{"@type":98,"interactionType":99,"userInteractionCount":40},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What task does the document evaluate for LLMs?","Question",{"text":108,"@type":109},"It evaluates how well GPT-4 and CodeLlama can generate, validate, and rectify Software Requirements Specification (SRS) drafts from natural language prompts.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How are GPT-4 and CodeLlama compared in the study?",{"text":113,"@type":109},"They are compared against human benchmarks using eight distinct criteria on an SRS for a university club management system.",{"name":115,"@type":106,"acceptedAnswer":116},"What do the experiments suggest about time savings?",{"text":117,"@type":109},"By repeating the generation exercise for four use cases, the study shows that LLMs can significantly reduce effort for entry-level engineers when producing and improving SRS documents.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},240853,1791330097,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":40,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":76},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Using LLMs in Software Requirements Specifications: An Empirical Evaluation  \nMadhava Krishna∗†, Bhagesh Gaur∗†, Arsh Verma†‡, Pankaj Jalote†  \n† Department of Computer Science, IIIT Delhi ‡ Wadhwani AI  \n{madhava20217, bhagesh20558, arsh17221, [jalote](jalote}@iiitd.ac.in)[}](jalote}@iiitd.ac.in)[@iiitd.ac.in](jalote}@iiitd.ac.in)  \narXiv :2404 . 17842v1 [ cs . SE] 27 Apr 2024  \nAbstract—The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama’s results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entrylevel software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.  \nIndex Terms—Requirements engineering, software requirements specifications, empirical research, large language models  \nI. INTRODUCTION  \nOne of the fundamental requirements for developing software is clearly defined objectives and communication of the same to the software developers. It is standard practice in the industry to prepare a Software Requirements Specification (SRS) document at the beginning of the project to ensure clear communication between all the stakeholders involved. Traditionally, this document is prepared and reviewed manually with contributions from multiple team members. This practice can take weeks or months but is earmarked as an essential time investment. However, with the advent of Large Language Models (LLMs), we find ourselves at a revolutionary moment where such ML models can assist in automating even the toughest technical tasks. In this work, we explore the efficacy of GPT-4 (ChatGPT) [1], and CodeLlama [2] in generating, validating, and rectifying these documents using natural language prompts describing the features of our in-  \n∗ Equal Contribution  \ntended final product. We also present evidence on the time saved by utilizing these models for the task.  \nSoftware Requirements Specifications are formally defined as a “specification for a particular software product, program, or set of programs that perform certain functions in a specific environment” containing details about the functionality, external interfaces, performance, attributes, and design constraints imposed on an implementation [3], [4] . The SRS is a comprehensive document that serves as the foundational blueprint of the entire software development lifecycle. It ensures alignment among all stakeholders by delineating a software venture’s expectations, operational methods, and inherent constraints, thus ensuring clarity regarding the software’s intended purpose, functionality, and limitations. This can expedite the development trajectory by preemptively diagnosing and fixing potential pitfalls, obviating the need for extensive rework or modifications. A robust SRS serve","cbCaie8hI4pWA0gh","https://ap.wps.com/l/cbCaie8hI4pWA0gh","pdf",335994,9,"English","# I. INTRODUCTION\n## Research questions and evaluation approach\n# II. RELATED WORKS","[{\"question\":\"What task does the document evaluate for LLMs?\",\"answer\":\"It evaluates how well GPT-4 and CodeLlama can generate, validate, and rectify Software Requirements Specification (SRS) drafts from natural language prompts.\"},{\"question\":\"How are GPT-4 and CodeLlama compared in the study?\",\"answer\":\"They are compared against human benchmarks using eight distinct criteria on an SRS for a university club management system.\"},{\"question\":\"What do the experiments suggest about time savings?\",\"answer\":\"By repeating the generation exercise for four use cases, the study shows that LLMs can significantly reduce effort for entry-level engineers when producing and improving SRS documents.\"}]","Using LLMs in Software Requirements Specifications - An Empirical Evaluation | PDF",1789166527]