TraceDev：一种用于需求到代码开发的可追溯性驱动的多智能体框架
TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development
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中文总结 AI 辅助
针对现有方法在自然语言需求到代码转换中存在的不足，提出TraceDev多智能体框架，通过五个特定角色智能体及可追溯性图，实现自动化软件开发，在两个数据集上的评估结果证明了其在需求到代码生成上的有效性。
中文摘要 AI 辅助
在现代软件开发中，大语言模型的快速发展使自然语言需求到可执行的仓库级代码的端到端转换越来越可行。但现有方法依赖简化指令，无法反映复杂场景且缺乏明确的需求可追溯机制。为此提出TraceDev，一个基于包含多个功能点和复杂语义的用例的自动化软件开发多智能体框架。它采用五个特定角色的智能体，验证智能体构建并维护一个异构可追溯性图来链接需求、设计模型和代码工件，以与前四个智能体交互。通过在两个数据集上与两种先进方法对比评估TraceDev，结果表明它在从需求生成仓库级代码方面有效。
英文摘要
In modern software development, the rapid advancement of Large Language Models (LLMs) has made the end-to-end transformation of Natural Language Requirements (NLRs) into executable repository-level code increasingly feasible. However, existing approaches typically rely on simplified instructions (e.g., single-sentence descriptions), failing to reflect complex software development scenarios. Moreover, they lack explicit requirement traceability mechanisms, making it difficult to precisely align and validate generated code against original requirements. To address these limitations, we propose TraceDev, a multi-agent framework for automated software development grounded in use cases that contain multiple functional points and complex semantics. TraceDev employs five role-specific agents, including a Requirement Refiner, Designer, Developer, Tester, and Validator. Notably, the Validator Agent constructs and maintains a heterogeneous traceability graph that links requirements, design models, and code artifacts for interacting with the preceding four agents. The traceability graph maintains consistency across various artifacts and serves as a structured context for efficient memory management, supporting reliable repository-level code generation. We evaluate TraceDev on two widely used datasets (including 125 use cases) compared with two state-of-the-art approaches. On the ETOUR dataset, TraceDev achieves a success rate of 53.63\%, outperforming baseline approaches by up to 186.63\%. A similar trend is observed on the SMOS dataset, where TraceDev attains a success rate of 56.82\%, exceeding baseline approaches by up to 340.80\%. These results demonstrate the effectiveness of TraceDev in repository-level code generation from requirements.
