[论文解读] SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks

Yucheng Zeng, Shupeng Li+12 more|arXiv (Cornell University)|Feb 28, 2026

Software Engineering Research被引用 0

一句话总结

SWE-Hub 引入一个端到端数据工厂，统一环境配置、可扩展任务合成和多样化任务生成，以规模化产出可执行的 SWE 任务。它在一个共享执行基底上构建三条任务产品线，用于修复、基于现实感的回归和长期仓库构建。

ABSTRACT

Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems from three fundamental challenges in existing pipelines: environments are brittle and difficult to reproduce across languages; synthesizing realistic, system-level bugs at scale is computationally expensive; and existing data predominantly consists of short-horizon repairs, failing to capture long-horizon competencies like architectural consistency. We introduce extbf{SWE-Hub}, an end-to-end system that operationalizes the data factory abstraction by unifying environment automation, scalable synthesis, and diverse task generation into a coherent production stack. At its foundation, the extbf{Env Agent} establishes a shared execution substrate by automatically converting raw repository snapshots into reproducible, multi-language container environments with standardized interfaces. Built upon this substrate, extbf{SWE-Scale} engine addresses the need for high-throughput generation, combining cross-language code analysis with cluster-scale validation to synthesize massive volumes of localized bug-fix instances. extbf{Bug Agent} generates high-fidelity repair tasks by synthesizing system-level regressions involving cross-module dependencies, paired with user-like issue reports that describe observable symptoms rather than root causes. Finally, extbf{SWE-Architect} expands the task scope from repair to creation by translating natural-language requirements into repository-scale build-a-repo tasks. By integrating these components, SWE-Hub establishes a unified production pipeline capable of continuously delivering executable tasks across the entire software engineering lifecycle.

研究动机与目标

• 解决用于训练和评估软件工程代理的可执行、可扩展数据稀缺问题。
• 将环境自动化、可扩展合成和多样化任务生成统一到一个生产栈中。
• 提供一个共享的执行底层，具备标准化接口与验证，以实现可重复的任务。
• 提供覆盖修复、回归现实性和长期仓库构建的多任务族。

提出的方法

• Env Agent 将原始仓库快照转换为可重复的多语言容器环境，具备标准化接口。
• SWE-Scale 引擎通过跨语言分析与自动验证生成大量本地化的 bug-fix 实例。
• Bug Agent 以用户风格的问题报告合成系统级回归，描述症状而不提供根因线索。
• SWE-Architect 将自然语言需求转化为可从零开始构建的仓库级任务，支持长期数据生成。
• 所有任务线路共用基于 Kubernetes 的执行底层、统一的任务模式以及可重复性的确定性验证门槛。

Figure 1: SWE-Hub architecture. Starting from raw source repositories, the Env Agent provisions a deterministic execution substrate—pinned container images, a unified verification entrypoint, and deterministic artifacts—to make code runnable and testable. On top of this substrate, three task generat

实验结果

研究问题

• RQ1 生产系统如何在多语言环境下将原始仓库可靠地转化为可执行、可验证的任务实例？
• RQ2 在统一验证器下，是否可以将可扩展合成与真实感 bug 生成与长期仓库构建任务集成？
• RQ3 哪种体系结构设计能够在 SWE 中实现跨语言可重复性、确定性环境 provisioning 以及可扩展的任务生成？

主要发现

• 提出一种统一的数据工厂架构，用于标准化环境设置、候选生成和可验证性，以产出可执行的 SWE 任务。
• 一个自动化的可重复性底座（Env Agent）将环境就绪与测试正确性解耦，并产生带有标准化入口点的版本化镜像。
• 三条产品线（SWE-Scale、Bug Agent、SWE-Architect）能够在同一底座上实现面向修复的合成、面向真实感的回归以及长期目标的仓库构建。
• 将验证视为核心可扩展性问题，具备无状态工作节点和跨多种生态系统的确定性、可机器解析的输出。

Figure 2: SWE-Hub Environment Layer pipeline. Phase 1 (Env Agent) performs environment provisioning and build readiness, identifying toolchains and installing dependencies to achieve environment readiness without requiring tests to pass. Phase 2 (Test Agent) establishes a unified verification interf