[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127632-en":3,"doc-seo-127632-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127632,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","An extension ofiStar for Machine Learning requirements by following the PRISE methodology","The expansion of Artificial Intelligence and Deep Learning has made Machine Learning (ML) commonplace in academia and enterprise, yet ML projects depend on both domain expertise and extensive algorithm and data-processing knowledge. This creates strong communication needs and complex requirement sets, especially when non-functional and quality aspects such as explainability, noise robustness, and equity must be captured. The paper addresses the lack of ML requirements modeling approaches by presenting the first i* extension, validated on two real-world projects, to support objective specification and the justification of suitable ML approaches.","Computer Standards & Interfaces 88 (2024) 103806  \n| An extension ofiStar for Machine Learning requirements by following the PRISE methodology\u003Cbr>Jose M. Barrera a,b, Alejandro Reina-Reina a,b, Ana Lavallea,∗, Alejandro Maté a, Juan Trujillo aa Lucentia Research Group, Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690, Spain\u003Cbr>b Lucentia lab, Av. Pintor Pérez Gil, 16, Alicante, 03540, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Requirements engineering Machine learning\u003Cbr>iStar\u003Cbr>Conceptual modeling |  | The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains. |  |\n\n1. Introduction  \nThe use of Artificial Intelligence (AI) has dramatically increased in recent years. Thanks to the latest advances and the proliferation of data science and deep learning, data-intensive problems can now be solved and better decisions can be made. One of the branches of AI is Machine Learning (ML). In this field, mathematical models learn weights to define the rules that guide the distribution function of a data set.  \nThe use of ML models can aid to answer questions related to classification such as ‘‘Will this person infected with COVID-19 survive with this health and demographic data?’’, regression ‘‘Which are the estimated sales of our company with these characteristics?’’ and clustering ‘‘Which are the common patterns of people infected by COVID-19?’’ among others.  \nDespite its widespread use and power, as [1] argues, requirements are a non-resolved challenge in ML projects. How well project requirements are translated into concepts, features and ML metrics depends entirely on the expertise of the data scientist or data analyst. To further aggravate the situation, capturing requirements in an ML project requires both technical knowledge of ML as well domain knowledge [2], making it difficult to find suitable candidates.  \nAccording to [3], requirements are paramount in ML projects. Incomplete or incorrect requirements capture can imply costly drawbacksin the project, which are commonly detected too late during implementation [4]. Consequently, there is a need to improve ML requirements capture. Given the involvement and interaction between domain and technical knowledge, the ideal ML requirements language is one tha","cbCaij9eEPRn8A39","https://ap.wps.com/l/cbCaij9eEPRn8A39","pdf",1809100,3,1,15,"English","en",105,"# Introduction\n## Motivation: ML projects need rigorous requirements capture\n## Role of requirements languages and i* framework\n## PRISE-driven extension for ML requirements modeling","[{\"question\":\"Why is capturing Machine Learning requirements considered a challenge in ML projects?\",\"answer\":\"Requirements translation into concepts, features, and ML metrics depends heavily on data scientists’ expertise, and capturing requirements needs both ML technical knowledge and domain knowledge, making suitable candidates hard to find.\"},{\"question\":\"What kinds of requirements matter specifically for ML projects?\",\"answer\":\"Beyond functional requirements like classification, clustering, or regression, ML projects must consider non-functional and quality aspects such as explainability, noise robustness, and equity.\"},{\"question\":\"What does the paper contribute regarding i* and ML requirements modeling?\",\"answer\":\"It proposes the first i* extension for capturing ML requirements and applies it to two real-world projects to support domain experts in specifying objectives and quality aspects, and to facilitate selection and justification of appropriate ML approaches.\"}]","An extension ofiStar for Machine Learning requirements by following the PRISE methodology | 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is capturing Machine Learning requirements considered a challenge in ML projects?","Question",{"text":76,"@type":77},"Requirements translation into concepts, features, and ML metrics depends heavily on data scientists’ expertise, and capturing requirements needs both ML technical knowledge and domain knowledge, making suitable candidates hard to find.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What kinds of requirements matter specifically for ML projects?",{"text":81,"@type":77},"Beyond functional requirements like classification, clustering, or regression, ML projects must consider non-functional and quality aspects such as explainability, noise robustness, and equity.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the paper contribute regarding i* and ML requirements modeling?",{"text":85,"@type":77},"It proposes the first i* extension for capturing ML requirements and applies it to two real-world projects to support domain experts in specifying objectives and 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