[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-426520-105":59,"doc-detail-426520-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","automatic-detection-of-ambiguous-terminology-for-software-requirements","Automatic Detection of Ambiguous Terminology for Software Requirements","","Identifying ambiguous requirements prevents costly design and implementation errors in software development. This work investigates lexical ambiguity detection for software requirement specifications and proposes automatic methods to identify potentially ambiguous concepts. The study targets overloaded and synonymous lexical ambiguities. Experiments on four real-world requirement collections show that the proposed ranking-based methods effectively detect ambiguous terminology and support engineers in revising specifications earlier in the life cycle.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automatic-detection-of-ambiguous-terminology-for-software-requirements/426520/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automatic-detection-of-ambiguous-terminology-for-software-requirements/426520.png","ImageObject",300,407,{"name":92,"@type":93},"Mimi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-29",true,{"@type":101,"interactionType":102,"userInteractionCount":8},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What problem does the paper address in software requirements engineering?","Question",{"text":111,"@type":112},"The paper addresses lexical ambiguity in software requirement specifications, which can lead to multiple interpretations and costly errors later in the project lifecycle.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"Which types of lexical ambiguities are the focus of the proposed methods?",{"text":116,"@type":112},"The methods focus on two types: overloaded ambiguity (a concept with different semantic meanings) and synonymous ambiguity (different concepts used interchangeably for the same meaning).",{"name":118,"@type":109,"acceptedAnswer":119},"How do the authors model the task of detecting ambiguous terminology?",{"text":120,"@type":112},"They formulate ambiguity detection as a ranking problem that orders important concepts by ambiguity scores, helping requirement engineers identify and revise the most ambiguous items.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},426520,1790695791,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":41},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Automatic Detection of Ambiguous Terminology for Software Requirements  \nYue Wang, Irene L. Manotas Gutirrez, Kristina Winbladh, and Hui Fang  \nDepartment of Electrical and Computer Engineering,  \nUniversity of Delaware,  \nNewark, DE 19716  \n{wangyue,imanotas,winbladh, [hfang}@udel.edu](hfang}@udel.edu)  \nAbstract. Identifying ambiguous requirements is an important aspect of software development, as it prevents design and implementation errors that are costly to correct. Unfortunately, few efforts have been made to automatically solve the problem. In this paper, we study the problem of lexical ambiguity detection and propose methods that can automatically identify potentially ambiguous concepts in software requirement specifications. Specifically, we focus on two types of lexical ambiguities, i.e., Overloaded and Synonymous ambiguity. Experiment results over four real-world software requirement collections show that the proposed methods are effective in detecting ambiguous terminology.  \nKeywords: Ambiguity detection, Software requirements, Overloaded ambiguity, Synonymous ambiguity  \n1 Introduction  \nA Software Requirements Specification (SRS) describes the required behaviour of a software product, and is often specified as a set of necessary requirements for project development. An ideal SRS should clearly state the requirements without introducing any ambiguities. Unfortunately, it is impossible to avoid the ambiguous SRSs since they are often described using natural languages.  \nA requirement is ambiguous if it can be interpreted in multiple ways. Ambiguous requirements can be a major problem in software development [4] . Project participants tend to subconsciously disambiguate requirements based on their own understanding without realizing that they are ambiguous. As a result, different interpretations often remain undiscovered until later stages of the software life-cycle, when design and implementation choices materialize the specific interpretations. It costs 50-200 times as much to correct an error late in a software project compared to when it was introduced [3] .  \nOne possible way of preventing ambiguous requirements is through manual inspection [17], which clearly is time-consuming and error prone. Consequently, it is important to study how to automatically detect ambiguous requirements in software requirement specifications (SRS) .  \nEstablishing a consistent usage of terminology early on in a project is imperative as it provides a vocabulary for the project and can greatly reduce misunderstandings. In  \n2 Automatic Detection of Ambiguous Terminology for Software Requirements  \nthis paper, we focus on the problem of lexical ambiguity detection. Specifically, we aim to detect terminology misuse such as overloaded and synonymous concepts. We use the word concept instead of term, because we consider both terms and phrases. A concept is overloaded if it refers to different semantic meanings and it is synonymous if several different concepts are used interchangeably to refer to the same semantic meaning (see Fig. 1). Note that overloaded concepts include both homonyms and polysemy.  \nOverloaded concept  \nSynonymous concepts  \nFig. 1. Overloaded and synonymous concepts.  \nWe propose to formulate the problem as a ranking problem that ranks all the important concepts from a SRS based on their ambiguity scores. The ranked list of concepts is expected to help requirement engineers to more efficiently identify ambiguous concepts and revise the SRS accordingly. One advantage of formulating the problem this way is to allow requirements engineers to decide how many concepts they want to go through based on their own situations. For example, some engineers may want to catchall ambiguous concepts while others may only have limited time to correct the most ambiguous ones. Once the ambiguous concepts are identified and rephrased, the SRS would have higher quality and can be better used in the subsequent stages ofthe project.  \nSpecifically,","cbCaidRNtWBD3SVr","https://ap.wps.com/l/cbCaidRNtWBD3SVr","pdf",273327,12,"English","# 1 Introduction\n# 2 Automatic Detection of Ambiguous Terminology for Software Requirements\n# 2 Related Work","[{\"question\":\"What problem does the paper address in software requirements engineering?\",\"answer\":\"The paper addresses lexical ambiguity in software requirement specifications, which can lead to multiple interpretations and costly errors later in the project lifecycle.\"},{\"question\":\"Which types of lexical ambiguities are the focus of the proposed methods?\",\"answer\":\"The methods focus on two types: overloaded ambiguity (a concept with different semantic meanings) and synonymous ambiguity (different concepts used interchangeably for the same meaning).\"},{\"question\":\"How do the authors model the task of detecting ambiguous terminology?\",\"answer\":\"They formulate ambiguity detection as a ranking problem that orders important concepts by ambiguity scores, helping requirement engineers identify and revise the most ambiguous items.\"}]","Automatic Detection of Ambiguous Terminology for Software Requirements | PDF",1790640816]