[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123934-pt":3,"doc-seo-123934-112":31,"detail-sidebar-cat-0-pt-112":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},123934,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",32,"Pesquisa e Relatórios","Aplicando Métodos de Aprendizagem de Máquina à Classificação de Requisitos","Engenharia de Requisitos (ER) é uma etapa crítica do desenvolvimento de software, responsável por definir e manter requisitos funcionais e não funcionais. Quando ocorre falha por requisitos incompletos, imprecisos ou mal classificados, surgem riscos de insucesso em projetos. Para mitigar esse problema, a dissertação integra Aprendizagem de Máquina (ML) à ER, usando modelos supervisionados e Active Learning para automatizar a classificação. A abordagem enfrenta o desafio de conjuntos de dados frequentemente não rotulados, reduzindo o esforço de anotação e acelerando a decisão.","DEPARTMENT OF COMPUTER SCIENCE  \nJOÃO PEDRO GONÇALVES AZEVEDO  \nMaster in Computer Science Engineering  \nAPPLYING MACHINE LEARNING METHODS TO REQUIREMENTS CLASSIFICATION DISSERTAÇÃO PARA OBTENÇÃO DO GRAU DE  \nMESTRE EM ENGENHARIA INFORMÁTICA  \nMASTER IN COMPUTER SCIENCE  \nNOVA University Lisbon  \nDEPARTMENT OF COMPUTER SCIENCE  \nAPPLYING MACHINE LEARNING METHODS TO REQUIREMENTS CLASSIFICATION  \nDISSERTAÇÃO PARA OBTENÇÃO DO GRAU DEMESTRE EM ENGENHARIA INFORMÁTICA  \nJOÃO PEDRO GONÇALVES AZEVEDO  \nMaster in Computer Science Engineering  \nAdviser: João Baptista da Silva Araújo Junior  \nFull Professor, NOVA University of Lisbon  \nCo-adviser: José Alberto Rodrigues Pereira Sardinha  \nAssistant Professor, Instituto Superior Técnico, University of Lisbon  \nExamination Committee  \nMASTER IN COMPUTER SCIENCE  \nNOVA University Lisbon  \nApplying Machine Learning Methods to Requirements Classification  \nCopyright © João Pedro Gonçalves Azevedo, NOVA School of Science and Technology, NOVA University Lisbon.  \nThe NOVA School of Science and Technology and the NOVA University Lisbon have the right, perpetual and without geographical boundaries, to file and publish this dissertation through printed copies reproduced on paper or on digital form, or by any other means known or that may be invented, and to disseminate through scientific repositories and admit its copying and distribution for non-commercial, educational or research purposes, as long as credit is given to the author and editor.  \nAcknowledgements  \nI would like to start by thanking my advisors, Professors JoãoAraújo and Alberto Sardinha, for all their support, guidance and availability throughout the entire process.  \nAdditionally , I would like to thank my family, who has always supported me and has been so proud of me throughout my journey. Mainly to my mother and sister, Lucília Nogueira and Ana Azevedo for helping me to become who I am today.  \nTo my girlfriend, Rita Silva, for helping me to lift my head during the less positive moments of this thesis, helping to overcome them.  \nAbstract  \nRequirements Engineering (RE) is an important phase of software development. This process consists of defining and maintaining the software requirements. These requirements can be classified into functional and non-functional requirements. However, when this process fails, due to incomplete, non-accurate, or even misclassified requirements, it can lead to project failures. To tackle this problem, Machine Learning (ML) techniques can be applied to help manage requirements. ML is a sub-field of Artificial Intelligence that can be used to aid the decision-making process by building automated models trained through samples of data. Thus, to attenuate these failures, will bring together RE and ML to automatically classify the requirements.  \nWe will use Supervised ML models and Active Learning (AL) . SL models requires large amounts of data to be trained efficiently. However, in most cases, the data sets used to train are unlabelled and since the amount of data is too large, it makes it difficult to manually label it, being this the main challenge to train supervised models. AL is a way to counter the problems related to SL by accurately selecting the examples to be labelled by the user and consequently used to train the model. By aggregating AL with SL, we can achieve a more efficient model than by labelling the entire training set. We will define an approach to apply ML and AL to classify requirements datasets systematically. Thus, we will be able to accelerate and automate the requirements classification process. Classifying the requirements into categories enables developers to focus more on the other stages of the development process. The requirements to be used in the classification process will be written as informal text or user stories.  \nKeywords: Requirements, Machine Learning, Active Learning, Software Engineering, Requirements Classification, Agile Development  \nRe sumo  \nA Engenhari","cbCaip3OZADsbJqL","https://ap.wps.com/l/cbCaip3OZADsbJqL","pdf",9353410,3,1,188,"Portuguese","pt",112,"# Agradecimentos\n# Resumo\n# Palavras-chave\n# Abstract\n# Keywords","[{\"question\":\"Quais são os principais problemas tratados na classificação de requisitos?\",\"answer\":\"A dissertação foca falhas causadas por requisitos incompletos, não precisos ou incorretamente classificados, que podem levar ao insucesso de projetos.\"},{\"question\":\"Que técnicas de machine learning serão usadas na abordagem proposta?\",\"answer\":\"Serão utilizados modelos de Supervised Learning e Active Learning para classificar requisitos automaticamente.\"},{\"question\":\"Como o Active Learning reduz o esforço de rotulagem dos dados?\",\"answer\":\"O Active Learning seleciona de forma precisa quais exemplos devem ser rotulados pelo utilizador, evitando rotular todo o conjunto de treino e tornando o processo mais eficiente.\"}]","Aplicando Métodos de Aprendizagem de Máquina à Classificação de Requisitos | 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são os principais problemas tratados na classificação de requisitos?","Question",{"text":76,"@type":77},"A dissertação foca falhas causadas por requisitos incompletos, não precisos ou incorretamente classificados, que podem levar ao insucesso de projetos.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Que técnicas de machine learning serão usadas na abordagem proposta?",{"text":81,"@type":77},"Serão utilizados modelos de Supervised Learning e Active Learning para classificar requisitos automaticamente.",{"name":83,"@type":74,"acceptedAnswer":84},"Como o Active Learning reduz o esforço de rotulagem dos dados?",{"text":85,"@type":77},"O Active Learning seleciona de forma precisa quais exemplos devem ser rotulados pelo utilizador, evitando rotular todo o conjunto de treino e tornando o processo mais 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