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.

翻译：大型语言模型智能体的最新进展为实现从自然语言需求到端到端软件开发的自动化提供了可能。然而，现有方法大多采用线性的瀑布式流程，过度简化了实际开发中的迭代特性，难以应对复杂的大规模项目。为克服这些局限，我们提出EvoDev，一种受特征驱动开发启发的迭代式软件开发框架。EvoDev将用户需求分解为一组具有用户价值的特征，并构建一个特征图——一个有向无环图，用于显式建模特征间的依赖关系。特征图中的每个节点维护着包括业务逻辑、设计和代码在内的多层次信息，这些信息沿着依赖关系传播，为后续开发迭代提供上下文。我们在具有挑战性的Android开发任务上评估EvoDev，结果表明其性能显著优于表现最佳的基线方法Claude Code，优势幅度达56.8%，同时在不同基础LLM上将单智能体性能提升了16.0%至76.6%，这凸显了依赖关系建模、上下文传播以及工作流感知的智能体设计对于复杂软件项目的重要性。我们的工作总结了设计迭代式、LLM驱动的开发框架的实践见解，并为未来基础LLM的训练提供了参考，以更好地支持迭代式软件开发。