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

Amin Beheshti

TL;DR

The paper argues that Natural Language-Oriented Programming (NLOP) can democratize software creation by marrying generative AI with natural language interfaces to translate high-level user intent into executable code. It contrasts LOP’s domain-specific languages with NLOP’s natural-language-based approach, detailing a framework that includes ProcessGPT, NLI, Prompt Engineering, AI-driven code generation, and AI QA within a microservices/API-first architecture. Key contributions include the architectural blueprint for transforming diverse programming data into reusable APIs, the integration of human-centered QA, and mechanisms for scalable, secure API layer management. The proposed model promises faster development cycles, broader participation, and more flexible, resilient software systems, particularly in business contexts that demand rapid prototyping and cross-disciplinary collaboration.

Abstract

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.

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

TL;DR

The paper argues that Natural Language-Oriented Programming (NLOP) can democratize software creation by marrying generative AI with natural language interfaces to translate high-level user intent into executable code. It contrasts LOP’s domain-specific languages with NLOP’s natural-language-based approach, detailing a framework that includes ProcessGPT, NLI, Prompt Engineering, AI-driven code generation, and AI QA within a microservices/API-first architecture. Key contributions include the architectural blueprint for transforming diverse programming data into reusable APIs, the integration of human-centered QA, and mechanisms for scalable, secure API layer management. The proposed model promises faster development cycles, broader participation, and more flexible, resilient software systems, particularly in business contexts that demand rapid prototyping and cross-disciplinary collaboration.

Abstract

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.
Paper Structure (21 sections, 4 figures)

This paper contains 21 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Language-Oriented Programming (LOP) vs. Natural Language-Oriented Programming (NLOP)
  3. Programming: The Age of Generative AI
  4. Business Value
  5. Generative Artificial Intelligence (AI)
  6. Evolution of Programming Paradigms
  7. Spaghetti Code
  8. Structured Programming
  9. Modular Programming
  10. Object-Oriented Programming
  11. Service-Oriented Computing
  12. Natural Language-Oriented Programming (NLOP): a New Computer Programming Model
  13. ProcessGPT: Understanding Programming Data Spaces for NLOP
  14. NLI: Natural Language Interface
  15. PE: Prompt Engineering
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Comparing Traditional Software Engineering, Machine Learning, and Natural Language-Oriented Programming Models
  • Figure 2: Understanding Generative AI, Foundation Models, and Large Language Models (LLMs).
  • Figure 3: Evolution of Programming Paradigms Over Time. This timeline illustrates the progression of major programming paradigms, highlighting key developments in language model features such as reusability and abstraction, which have significantly improved through the decades.
  • Figure 4: Proposed framework for Natural Language-Oriented Programming.