# Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creation

arXiv: https://arxiv.org/abs/2406.05409 · HTML: https://mlsanity.com/paper/2406.05409/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [From Copilot to Pilot: Towards AI Supported Software Development](https://mlsanity.com/paper/2303.04142/) (2023)
- [ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence](https://mlsanity.com/paper/2306.01771/) (2023)

## Built on by (most cited first, 4 of 4)

- [Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda](https://mlsanity.com/paper/2507.21928/) (2025)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing](https://mlsanity.com/paper/2607.12619/) (2026)
- [Agentic Synthesis against Counterexample-Supplemented Sketches](https://mlsanity.com/paper/2607.15854/) (2026)

## Abstract (arXiv)

As generative Artificial Intelligence (AI) technologies evolve, they offer unprecedented potential to automate and enhance various tasks, including coding. Natural Language-Oriented Programming (NLOP), a vision introduced in this paper, harnesses this potential by allowing developers to articulate software requirements and logic in their natural language, thereby democratizing software creation. This approach streamlines the development process and significantly lowers the barrier to entry for software engineering, making it feasible for non-experts to contribute effectively to software projects. By simplifying the transition from concept to code, NLOP can accelerate development cycles, enhance collaborative efforts, and reduce misunderstandings in requirement specifications. This paper reviews various programming models, assesses their contributions and limitations, and highlights that natural language will be the new programming language. Through this comparison, we illustrate how NLOP stands to transform the landscape of software engineering by fostering greater inclusivity and innovation.