# An Empirical Study on Low-Code Programming using Traditional vs Large Language Model Support

arXiv: https://arxiv.org/abs/2402.01156 · HTML: https://mlsanity.com/paper/2402.01156/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2024.

## Built on by (most cited first, 7 of 7)

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- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)
- [Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++](https://mlsanity.com/paper/2508.16419/) (2025)
- [TCEval: Using Thermal Comfort to Assess Cognitive and Perceptual Abilities of AI](https://mlsanity.com/paper/2512.23217/) (2025)
- [Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem](https://mlsanity.com/paper/2606.29116/) (2026)

## Abstract (arXiv)

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With the huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has begun to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers' discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations, and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.