研究探讨智能体应如何看待代码：五种代码表示工作流对 Agentic 软件工程的影响
How Should Agents See Code? Rethinking Code Representations for Agentic Software Engineering
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论文研究基于仓库地图的代码表示工作流对 LLM 智能体软件工程任务的影响，比较抽象语法树、调用图、模块依赖、文档增强检索和混合五种工作流，用 Claude Code 与 Haiku 4.5 在 LinuxFLBench、SWE-bench Pro、SWT-bench、SecBench 四个任务上对比原始工具基线。
正文 · 原文
Abstract:LLM agents are increasingly used for repository-level software engineering tasks. However, most agents still access repositories through raw files, lexical search, and shell commands, treating code primarily as text. This paper investigates map-based code-representation workflows as a design variable in agentic software engineering, examining how different repository views change agents' retrieval behavior, task success, token use, and latency.
In this paper, we study five code-representation workflows: abstract syntax trees, call graphs, module dependencies, documentation-enriched search, and a hybrid workflow that combines them. Each workflow exposes a repository map that the agent can query during a task. We evaluate Claude Code with Haiku 4.5 against a raw-tool baseline on four repository-level tasks: fault localization with LinuxFLBench, repository-level code generation and repair with SWE-bench Pro, test generation with SWT-bench, and security patching with SecBench.
The results show that representation workflows are most useful as token-saving tools when localization is the bottleneck, while their success gains narrow when the decisive step is patch or test authoring. On LinuxFLBench, all five workflows improve recall@10 over the baseline (0.757-0.829 vs. 0.717) and reduce token usage by 25-39%. On SWE-bench Pro, resolution remains close to the baseline (44.3-50.6% vs. 49.0%) while token usage drops by 17-26%. On SecBench, Callgraph has the highest patch-resolution rate in this sample (34.3% vs. 28.9%) and uses fewer tokens, but all workflows increase latency. On SWT-bench, Hybrid and Docs have the highest reproducing-test rates in this sample (47.8% and 47.2% vs. 44.7%), but all workflows use more tokens and take longer. Overall, map-based workflows change both what the agent inspects and how much it spends, but their benefits are task-dependent rather than universal.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07286 [cs.SE] |
| (or arXiv:2611.07286v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07286 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Melika Sepidband [view email]
[v1]
Thu, 5 Nov 2026 01:08:43 UTC (759 KB)
来源：arXiv cs.AI · arxiv.org