[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119117-en":3,"doc-seo-119117-105":30,"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":4,"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":27,"seo_description":14,"update_tm":28,"read_time":29},119117,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",6,"Technology","Using Machine Learning and Simplified Functional Measures to Estimate Software Development Effort","Functional size measures are often used as a foundation for estimating software development effort because they can be derived early. Simplified functional measurement methods reduce cost and enable estimation when full functional requirements are not yet fully known. Machine learning has shown success for effort estimation, but the empirical value of combining it with simplified functional measures had not been thoroughly evaluated. This study investigates accuracy impacts for new development, extensions, and modifications across multiple ML techniques, comparing traditional full measures versus simplified ones.","Received 2 September 2024, accepted 23 September 2024, date of publication 1 October 2024, date of current version 9 October 2024. Digital Object Identifier 10.1109/ACCESS.2024.3471428  \nUsing Machine Learning and Simplified Functional Measures to Estimate Software Development Effort  \nLUIGI LAVAZZA1,(Senior Member, IEEE), ANGELA LOCORO2, AND ROBERTO MELI3 1Dipartimento di Scienze Teoriche e Applicate, Università degli Studi dell’Insubria, 21100 Varese, Italy  \n2Dipartimento di Economia e Management, Università degli Studi di Brescia, 25100 Brescia, Italy  \n3DPO—Data Processing Organization, 00100 Rome, Italy Corresponding author: Luigi Lavazza ([luigi.lavazza@uninsubria.it](luigi.lavazza@uninsubria.it))  \nThis work was supported in part by the ‘‘Fondo di Ricerca d’Ateneo’’ funded by the Università degli Studi dell’Insubria.  \nABSTRACT Functional size measures are often used as the basis for estimating development effort, because they are available in the early stages of software development. Several simplified measurement methods have also been proposed, both to decrease the cost of measurement and to make functional size measurement applicable when functional user requirements are not yet known in full detail. Lately, machine learning techniques have been successfully used for software development effort estimation, but the usage of machine learning techniques in combination with simplified functional size measures has not yet been empirically evaluated. This paper aims to fill this gap: it reports to what extent functional size measures can be simplified, without decreasing the accuracy of effort estimates obtained via machine learning techniques. The reported evaluation addresses separately the effort models concerning (i) new software developed from scratch,(ii) software extensions obtained by adding new functionality, and (iii) software modifications that required also changing and possibly removing functionalities. We carried out an empirical study, in which effort estimation models were built via multiple Machine Learning techniques, using both traditional full-fledged functional size measures and simplified measures. Our study shows that using simplified functional size measures in place of traditional functional size measures for effort estimation does not yield practically relevant differences in accuracy. Therefore, software project managers can consider analyzing only a small and specific part of functional user requirements to get measures that effectively support effort estimation.  \nINDEX TERMS Software effort estimation, functional size measurement, function point analysis, machine learning.  \nI. INTRODUCTION  \nFunction Point Analysis (FPA) was introduced to yield a measure of software size based exclusively on functional requirements specifications [1] . Accordingly, functional size measures (FSMs) are widely used for estimating software development effort, mainly because they are available in the early stages of development, when effort estimates are most needed.  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Pinjia Zhang .  \nHowever, deriving FSMs requires that complete and detailed specifications are available; in addition, the measurement takes a relatively long time and requires highly qualified measurers. For all these reasons, a few ‘‘simplified’’ measures have been proposed. These measures are simpler and quicker, and applicable when fully detailed software specifications are not yet available. Among the simplified measures are Simple Function Points (SFP) [2](formerly known as SiFP [3]) and the sheer number of transaction functions.  \nSimplified measures consider a smaller amount of information than traditional Function Points, hence they maybe used to approximate traditional measures of functional  \nVOLUME 12, 2024  \n􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.  \nFor mor","cbCaipbnLtZl9Eh7","https://ap.wps.com/l/cbCaipbnLtZl9Eh7","pdf",1299877,1,19,"English","en",105,"# Introduction\n## Motivation for functional size measures\n## Limits of full Function Point Analysis\n## Role of simplified measures\n## Research questions and study scope","[{\"question\":\"Why are functional size measures used for estimating software development effort?\",\"answer\":\"They provide a way to quantify software size based on functional requirements and can be obtained early, when effort estimation is most needed.\"},{\"question\":\"What gap does the paper address regarding machine learning and simplified functional measures?\",\"answer\":\"It evaluates, empirically, whether simplified functional size measures can replace traditional full-fledged measures when building effort estimation models using machine learning.\"},{\"question\":\"Which project types are evaluated in the empirical study?\",\"answer\":\"The study separates effort models for new software developed from scratch, software extensions adding new functionality, and software modifications that may also change or remove functionalities.\"}]","Using Machine Learning and Simplified Functional Measures to Estimate Software Development Effort | 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are functional size measures used for estimating software development effort?","Question",{"text":75,"@type":76},"They provide a way to quantify software size based on functional requirements and can be obtained early, when effort estimation is most needed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What gap does the paper address regarding machine learning and simplified functional measures?",{"text":80,"@type":76},"It evaluates, empirically, whether simplified functional size measures can replace traditional full-fledged measures when building effort estimation models using machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which project types are evaluated in the empirical study?",{"text":84,"@type":76},"The study separates effort models for new software developed from scratch, software extensions adding new functionality, and software modifications that may also change or remove 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