Low-Code Development Enhancement Integrating Large Language Models for Intelligent Code Assistance in Oracle APEX - Keshireddy, Srikanth Reddy (Indian Journal of Information Sources and Services, 2025)
The growing use of low-code platforms within the business sphere has spurred the need for real-time, intelligent code help tools that can effectively integrate citizen developers with expert programmers. This research investigates the application of Large Language Models (LLMs) into Oracle APEX with the goal of improving developer productivity and minimizing the cognitive effort required during task execution. We created a novel system designed to operate within the Oracle APEX environment that includes an LLM-powered code suggestion engine. This system is able to respond to natural language queries by providing context-aware code snippets in PL/SQL, JavaScript, and SQL. Furthermore, it dynamically responds to page items, session states, and application metadata in real time. Our architecture integrates Oracle APEX's RESTful interfaces with external LLMs through a modular API orchestration layer to enable effortless generation of accurate deployable code. An extensive experimental evaluation with 48 developers from different seniority levels was performed, including tasks from UI sketching to business logic coding. Achieved results indicate an average of 41 percent reduction in completion time and 34 percent reduction in manual code cut editing. Structured logical scenarios achieved over 90 percent syntax accuracy, and users reported high confidence in the system outputs. The analysis also focuses on token consumption, domain-specific error patterns, and code review log feedback loops. The primary objective of this research study was to illustrate the profound impacts information technologies, particularly LLMs, had on the speed of achieving low-code application development with Oracle APEX. In addition, the study intends to demonstrate the set of strategies proposed suited for the consistent implementation of intelligent assistants that aid in elevating code quality while reducing barriers, enabling enterprise-level application delivery with minimal manual effort preconfigured.