Investigation on Integration of Machine Learning Techniques into LC/NC Platforms for Code Review, Quality Assessment, and Error Detection Automation

The advent of low-code and no-code platforms has transformed software development by allowing quick application creation with minimum manual coding. The growing low-code and no-code platform options allow non-professional developers to quickly build applications but also pose significant issues in maintaining effective quality control. Conventional human code review methods are comprehensive but typically require a lot of resources and slow down the fast development speed promoted by low-code and no-code platforms. This study explores the incorporation of several machine learning methods into low-code and no-code platforms to automate code review, quality evaluation, and error identification, ultimately improving development speed and application quality.