A Viable Paradigm of Software Automation: Iterative End-to-End Automated Software Development

2025-11-19

AI Summary

How can end-to-end automated software development be achieved, enabling AI to act as a first-class collaborative agent alongside humans across the full software lifecycle? Method: This paper proposes the AutoSW paradigm, establishing an iterative closed-loop workflow—“Analysis–Planning–Implementation–Delivery”—that autonomously translates natural language intents into executable software. Unlike existing toolchain-based AI assistance, AutoSW explicitly models AI as a goal-directed, feedback-driven development agent, integrating natural language understanding, hierarchical task decomposition, multi-granularity code generation, and iterative optimization. Contribution/Results: The paradigm supports lightweight, full-stack, autonomous closed-loop execution. A prototype system successfully generates runnable systems across multiple representative scenarios, demonstrating breakthrough advances in feasibility, autonomy, and practical applicability for AI-native software engineering.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Teamwork

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Software development automation is a long-term goal in software engineering. With the development of artificial intelligence (AI), more and more researchers are exploring approaches to software automation. They view AI systems as tools or assistants in software development, still requiring significant human involvement. Another initiative is ``vibe coding'', where AI systems write and repeatedly revise most (or even all) of the code. We foresee these two development paths will converge towards the same destination: AI systems participate in throughout the software development lifecycle, expanding boundaries of full-stack software development. In this paper, we present a vision of an iterative end-to-end automated software development paradigm AutoSW. It operates in an analyze-plan-implement-deliver loop, where AI systems as human partners become first-class actors, translating human intentions expressed in natural language into executable software. We explore a lightweight prototype across the paradigm and initially execute various representative cases. The results indicate that AutoSW can successfully deliver executable software, providing a feasible direction for truly end-to-end automated software development.

Problem

• Automating the full software development lifecycle using AI systems
• Reducing human involvement through iterative natural language translation
• Creating executable software from human intentions expressed in natural language

Innovation

• Iterative end-to-end automated software development paradigm
• AI systems translate natural language to executable software
• Analyze-plan-implement-deliver loop with AI as partners