SEAINov 4, 2025

EvoDev: An Iterative Feature-Driven Framework for End-to-End Software Development with LLM-based Agents

arXiv:2511.02399v11 citationsh-index: 13
Originality Highly original
AI Analysis

This work addresses the challenge of scaling LLM-driven software development for complex projects, offering a practical framework that is incremental over existing agent-based methods.

The paper tackled the problem of automating end-to-end software development with LLM-based agents by addressing limitations of linear pipelines, proposing EvoDev, an iterative framework inspired by feature-driven development, and showed it outperformed the best baseline by 56.8% and improved single-agent performance by 16.0%-76.6% on Android tasks.

Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipelines, which oversimplify the iterative nature of real-world development and struggle with complex, large-scale projects. To address these limitations, we propose EvoDev, an iterative software development framework inspired by feature-driven development. EvoDev decomposes user requirements into a set of user-valued features and constructs a Feature Map, a directed acyclic graph that explicitly models dependencies between features. Each node in the feature map maintains multi-level information, including business logic, design, and code, which is propagated along dependencies to provide context for subsequent development iterations. We evaluate EvoDev on challenging Android development tasks and show that it outperforms the best-performing baseline, Claude Code, by a substantial margin of 56.8%, while improving single-agent performance by 16.0%-76.6% across different base LLMs, highlighting the importance of dependency modeling, context propagation, and workflow-aware agent design for complex software projects. Our work summarizes practical insights for designing iterative, LLM-driven development frameworks and informs future training of base LLMs to better support iterative software development.

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