[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116881-en":3,"doc-seo-116881-105":30,"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":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},116881,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","PARSIMONIOUS MACHINE LEARNING MODELS IN REQUIREMENTS ELICITATION TECHNIQUES SELECTION - Article","The research develops machine learning methods for selecting requirement elicitation techniques in IT projects, aiming to predict which technique to use while relying on as few predictor variables as possible without a meaningful drop in prediction quality. Parsimonious candidate models are designed from practitioners’ experience data, their accuracy is assessed, and a best-candidate selection algorithm is constructed. Results show candidate models with reduced features to mitigate overfitting and a single selected model with sufficient performance, validated on four datasets.","DOI: 10.20998/2079-0023.2023.01.13 UDC 004.415.25  \nO. L. SOLOVEI, Candidate of Technical Sciences (Ph.D.), Kyiv National University of Construction and Architecture, Associate Professor at the Department of Information Technology for Design and Applied Mathematics, Kyiv, Ukraine, e-mail: [solovey.ol@knuba.edu.ua](solovey.ol@knuba.edu.ua); ORCID: [https://orcid.org/0000-0001-8774-7243](https://orcid.org/0000-0001-8774-7243)  \n[D](D). A. GOBOV, Candidate of Technical Sciences (Ph.D.), National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Associate Professor at the Department of Computer Science and Software Engineering of the Faculty of Informatics and Computer Science, Kyiv, Ukraine, e-mail: [d.gobov@kpi.ua](d.gobov@kpi.ua); ORCID: [https://orcid.org/0000-0001-9964-0339](https://orcid.org/0000-0001-9964-0339)  \nPARSIMONIOUS MACHINE LEARNING MODELS IN REQUIREMENTS ELICITATION TECHNIQUES  \nSELECTION  \nThe subject of research in the article is machine learning algorithms used for requirement elicitation technique selection. The goal of the work is to build effective parsimonious machine learning models to predict the using particular elicitation techniques in IT projects that allow using as few predictor variables as possible without a significant deterioration in the prediction quality. The following tasks are solved in the article: design an algorithm to build parsimonious machine learning candidate models for requirement elicitation technique selection based on gathered information on practitioners'experience, assess parsimonious machine learning model accuracy, and design an algorithm for the best candidate model selection. The following methods are used: algorithm theory, statistics theory, sampling techniques, data modeling theory, and science experiments. The following results were obtained: 1) parsimonious machine learning candidate models were built for the requirement elicitation technique selection. They included less number of features that helps in the future to avoid overfitting problems associated with the best-fit models; 2) according to the proposed algorithm for best candidate selection – a single parsimonious model with satisfied performance was chosen. Conclusion: An algorithm is proposed to build parsimonious candidate models for requirement elicitation technique selection that avoids the overfitting problem. The algorithm for the best candidate model selection identifies when a parsimonious model's performance is degraded and decides on the suitable model's selection. Both proposed algorithms were successfully tested with four datasets and can be proposed for their extensions to others.  \nKeywords: requirements elicitation techniques, Bayesian Information Criterion, Bayes factor grades, log-likelihood, parsimonious model.  \nIntroduction. Business analysis as an extension of requirements engineering is crucial to software development. The main business analysis deliverables are requirements and designs used as a basis for solution implementation, testing, and deployment. In turn, the critical input for the tasks of analysis, specification, and modeling of requirements and design for software is the information collected during the elicitation. Standard approaches to the requirements-gathering process have been systematized and described in the form of dozens of standard elicitation techniques. Industrial guidelines and empirical studies contain detailed descriptions of the techniques' elements and usage considerations but do not provide an elicitation selection process [1] .  \nConsequently, one of the challenges for business analysts/requirement engineers, especially novice ones, is the selection of the appropriate requirements elicitation techniques that best fit their project. As a result, some of them are misused, others are never used, and only a few are constantly applied. To solve the problem, a machine learning model to predict/recommend using the following elicitation te","cbCaijgelSRrPrr3","https://ap.wps.com/l/cbCaijgelSRrPrr3","pdf",803140,1,7,"English","en",105,"# Introduction\n## Requirements elicitation technique selection problem\n# Related work and limitations\n## Best-fit model overfitting risk\n# Proposed approach\n## Building parsimonious candidate models\n## Best-candidate selection algorithm\n# Experimental validation and results\n## Dataset testing and performance\n# Conclusion","[{\"question\":\"What is the main goal of the proposed research?\",\"answer\":\"To build effective parsimonious machine learning models that predict which requirements elicitation technique to use in IT projects while using as few predictor variables as possible.\"},{\"question\":\"Which requirements elicitation techniques are considered in the study?\",\"answer\":\"The study targets Interviews, Document Analysis, Process Analysis, and Interface Analysis, chosen according to project context.\"},{\"question\":\"How does the approach address the overfitting problem of best-fit models?\",\"answer\":\"It constructs candidate parsimonious models with fewer features and then selects a single model when its performance is not degraded, avoiding the complexity that leads to overfitting.\"}]","PARSIMONIOUS MACHINE LEARNING MODELS IN REQUIREMENTS ELICITATION TECHNIQUES SELECTION - 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