自进化编码智能体

摘要

大型语言模型正日益嵌入软件工程工作流程，作为能够检查代码仓库、调用工具、执行测试、调试故障并生成补丁的编码代理。然而，尽管软件开发是一个动态且富含反馈的过程——代码仓库不断演进、依赖关系持续变化、测试会失败、修复尝试会留下可复用的经验——大多数现有代理在部署后仍基本保持静态。这一矛盾催生了关于自我进化编码代理（self-evolving coding agents）的日益增长的研究工作，这类代理通过从先前的编码交互中更新其框架、记忆、技能、工具、模型或协作结构，来改进其未来的行为。在本综述中，我们对该新兴领域进行了系统性的综合梳理。我们首先定义自我进化编码代理，并将其与传统编码代理及通用自我进化代理区分开来。随后，我们构建了一个以对象为中心的分类体系，用以刻画这些系统中进化的对象，并通过两个正交视角加以补充：进化发生的时间以及驱动进化的软件特定证据。综合现有文献，我们发现可执行反馈、代码仓库级上下文和编码轨迹使软件工程成为一个独特的自然领域，适合代理自我进化，但同时也引入了反馈可靠性、基准过拟合、安全性、可维护性、成本和泛化能力等方面的新挑战。通过围绕这些维度组织现有工作，本综述旨在厘清自我进化编码代理的概念边界，并为设计更具适应性、更可靠且更具软件感知能力的智能体系统奠定基础。我们收集的论文可见于 https://github.com/zhouhao1024/Awesome-Self-Evolving-Coding-Agents。

English

Large language models are increasingly embedded in software engineering workflows as coding agents that can inspect repositories, invoke tools, execute tests, debug failures, and generate patches. Yet most existing agents remain largely static after deployment, even though software development is a dynamic, feedback-rich process in which repositories evolve, dependencies change, tests fail, and repair attempts leave reusable experience. This tension has motivated a growing body of work on self-evolving coding agents, where the agent improves its future behavior by updating its framework, memory, skills, tools, models, or collaboration structures from prior coding interactions. In this survey, we provide a systematic synthesis of this emerging area. We first define self-evolving coding agents and distinguish them from conventional coding agents and general self-evolving agents. We then develop an object-centered taxonomy that characterizes what evolves in these systems, and complement it with two orthogonal perspectives: when evolution occurs and what software-specific evidence drives it. Across the literature, we find that executable feedback, repository-level context, and coding trajectories give software engineering a distinctive role as a natural domain for agent self-evolution, but also introduce new challenges in feedback reliability, benchmark overfitting, safety, maintainability, cost, and generalization. By organizing existing work around these dimensions, this survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems. The papers we collect can be found at https://github.com/zhouhao1024/Awesome-Self-Evolving-Coding-Agents.