Role of machine learning in low-code/no-code platforms : a systematic literature review of opportunities and challenges

Abstract

This 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 presents a 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.