[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128331-en":3,"doc-seo-128331-105":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},128331,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Role of Machine Learning in Low-Code/No-Code Platforms - A Systematic Literature Review of Opportunities and Challenges","This thesis examines how machine learning (ML) is applied within low-code/no-code (LC/NC) development platforms, enabling end users to build applications more easily. A systematic literature review synthesizes recent research to identify opportunities, advantages, benefits, and the primary challenges and obstacles of integrating ML into LC/NC workflows. Findings indicate improvements in automation, decision-making, and user experience, while also highlighting risks such as model transparency, data quality, and scalability. It further outlines research gaps and future directions for stronger ML integration.","Role of Machine Learning in Low-Code/No-Code Platforms: A Systematic Literature Review of Opportunities and Challenges  \nLappeenranta–Lahti University of Technology LUT  \nMaster’s Programme in Software Product Management and Business, Master’s thesis  \n2025  \nJavad Khaleghi Hozhabrasa  \nExaminer: Associate Professor, Damian Kedziora and Researcher, Nasren Azad  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Engineering Sciences  \nMaster’s Program in Software Product Management and Business  \nJavad Khaleghi Hozhabrasa  \nRole of Machine Learning in Low-Code/No-Code Platforms: A Systematic Literature Review of Opportunities and Challenges  \nMaster’s thesis 2025  \n49 pages, 2 figures and 8 tables  \nExaminer: Associate Professor, Damian Kedziora and Researcher, Nasren Azad  \nKeywords: Low-Code (LC), No-Code (NC), Machine Learning (ML), LC/NC Platforms, Systematic Literature Review (SLR)  \nThis thesis investigates how machine learning (ML) is used in low-code/no-code (LC/NC) platforms. These platforms allow end users to develop applications more easily. It presentsa systematic literature review of recent studies to explore the main opportunities, advantages, benefits, challenges and obstacles of combining ML with LC/NC development. The results represents that ML can improve automation, decision-making, and user experience in LC/NC tools. However, there exists challenges related to model transparency, data quality, and system scalability. Moreover, this thesis discusses research gaps and possible future directions to improve ML integration into LC/NC platforms.  \nACKNOWLEDGEMENTS  \nI am truly thankful to my supervisors, Damian Kedziora and Nasren Azad, for their continuous assistance, kind guidance, valuable suggestions, and constructive comments that helped me improve my thesis.  \nSYMBOLS AND ABBREVIATIONS  \nAI  \nEC  \nLC  \nLLM  \nML  \nNC  \nNLP  \nPRISMA  \nRPA  \nQA  \nRQ  \nSLR  \nArtificial Intelligence  \nExclusion Criteria  \nLow-Code  \nLarge Language Models  \nMachine Learning  \nNo-Code  \nNatural Language Processing  \nPreferred Reporting Items in a Systematic Review and Meta-Analysis Robotic Process Automation  \nQuality Assurance  \nResearch Question  \nSystematic Literature Review  \nTABLE OF CONTENTS  \nABSTRACT........................................................................................................................... 2  \nACKNOWLEDGEMENTS................................................................................................... 3  \nSYMBOLS AND ABBREVIATIONS..................................................................................4  \nTABLE OF CONTENTS....................................................................................................... 5  \nDECLARATIONS ................................................................................................................. 7  \n1 Introduction .................................................................................................................... 8  \n1.1 Motivation and Importance of ML Integration in LC/NC Platforms ....................9  \n1.2 Problem Statement and Research Gap ................................................................. 10  \n1.3 Research Objectives and Questions ..................................................................... 12  \n1.4 Evaluation Focus: Benefits, Opportunities, Risks and Challenges...................... 13  \n1.5 Thesis Structure ................................................................................................... 14  \n2 Research Method .......................................................................................................... 15  \n2.1 Research Design .................................................................................................. 15  \n2.2 Data Sources and Search Queries ........................................................................ 15  \n2.2.1 Data Sources ...............................................................","cbCaii0nusF7CWnv","https://ap.wps.com/l/cbCaii0nusF7CWnv","pdf",1599229,1,49,"English","en",105,"# Introduction\n## Motivation and Importance of ML Integration in LC/NC Platforms\n## Problem Statement and Research Gap\n## Research Objectives and Questions\n## Evaluation Focus: Benefits, Opportunities, Risks and Challenges\n## Thesis Structure\n# Research Method\n## Research Design\n## Data Sources and Search Queries\n## Inclusion and Exclusion Criteria\n## Literature Collection Process\n## Data Extraction, Quality and Relevance Assessment\n## Data Analysis\n## Threats to Validity\n# Results\n## Integration Patterns\n## Mapping the Review","[{\"question\":\"What is the main focus of the thesis?\",\"answer\":\"The thesis investigates how machine learning is used in low-code/no-code platforms and evaluates the opportunities, advantages, benefits, and obstacles of this integration.\"},{\"question\":\"What benefits does ML integration bring to LC/NC platforms?\",\"answer\":\"The results indicate that ML can improve automation, decision-making, and user experience in LC/NC tools.\"},{\"question\":\"What challenges are identified when combining ML with LC/NC development?\",\"answer\":\"Key challenges include model transparency, data quality, and system scalability, which can limit effective adoption.\"}]","Role of Machine Learning in Low-Code/No-Code Platforms - 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