SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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 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 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, 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. 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, 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.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 23 references that are in the map. First posted on arXiv 28 Feb 2026.
Builds on
17 of 23 references.
Closest ancestors · 8
Same direction, newest first
- 2025SWE-RM: Execution-free Feedback For Software Engineering Agents26 Dec
- 2025Toward Training Superintelligent Software Agents through Self-Play SWE-RL21 Dec
- 2025SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios20 Dec
- 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents14 Dec
- 2025Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs24 Jun
- 2025SWE-smith: Scaling Data for Software Engineering Agents30 Apr
- 2025R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents9 Apr
- 2024Training Software Engineering Agents and Verifiers with SWE-Gym30 Dec
Other references · 9
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,274
- 2025Multi-SWE-bench: A Multilingual Benchmark for Issue Resolvingcited by 166
- 2025SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositoriescited by 30
- 2026SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolvingcited by 30
- 2025SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositoriescited by 28
- 2025SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugscited by 22
- 2025Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agentscited by 16
- 2026SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoringcited by 11
- 2025Multi-Docker-Eval: A `Shovel of the Gold Rush' Benchmark on Automatic Environment Building for Software Engineeringcited by 7
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnessescited by 129
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Applicationcited by 8
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 1
- 2026CodeMidas: Scaling Agentic Coding RL Environments from Code Itselfcited by 1
- 2026CodeMidas: Scaling Agentic Coding RL Environments from Code Itself18 Sep 2026
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application10 Jun 2026
- 2026Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses28 Apr 2026
Similar, not cited
Close in wording, no citation either way
- 2026SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
- 2026SWE-World: Building Software Engineering Agents in Docker-Free Environments
- 2025Training Versatile Coding Agents in Synthetic Environments
- 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
- 2026SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
- 2026DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch
- 2026SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration
- 2026Immersion in the GitHub Universe: Scaling Coding Agents to Mastery