Design and Implementation of an AI-Assisted Code Review Tool for Low-Code Platforms to Improve Quality and Security

Abstract

Low-code platforms like Mendix fast-tracks application development but, due to limited review mechanisms, face challenges in sustaining the code quality and security. Existing code review approaches are not optimized for visual cues, model-driven workflows, increasing the possibility of logical, security, and performance issues introduced by citizen developers. This research introduces an AI-assisted code review tool that combines GPT-4 and Claude Opus 4 for workflow analysis and defect detection in low-code environments. The approach evolved from few-shot prompting to workflow-oriented fine-tuning, resulting in improved analytical precision and reliability. The tool was further enhanced to perform business gap assessments and deliver user-friendly, structured feedback via a pluggable React-based widget integrated into the Mendix environment. The evaluation of the tool demonstrated an average precision of 84.5% and an average recall of 84.8% and an F1 score between (0.82-0.87), with workflow-based fine-tuning outperforming few-shot learning. A preliminary usability study with 25 developers demonstrated a 90% satisfaction rate and approximately 50% reduction in issue resolution time. Proxy validation using generative AI models was performed due to the limited availability of Mendix domain experts. These findings highlight the capability of AI-assisted code review to enhance workflow quality, strengthen application security, and improve developer productivity in low-code environments.