SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks

2026-02-28

AI Summary

This work addresses critical limitations in current research on software engineering agents, which suffers from a lack of executable, scalable, and realistic training and evaluation data—particularly in environment reproducibility, system-level bug synthesis, and long-horizon task modeling. To overcome these challenges, we propose SWE-Hub, an end-to-end unified production system featuring three key innovations: Env Agent for reproducible, multi-language containerized environments; Bug Agent for generating cross-module system-level regression tasks; and SWE-Architect for translating natural language requirements into repository-scale build tasks. By integrating automated environment setup, defect synthesis, and task generation, SWE-Hub establishes a high-throughput, high-fidelity pipeline that spans multiple programming languages and the full software development lifecycle, substantially enhancing the scale, realism, and diversity of executable software engineering tasks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 \textbf{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 \textbf{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, \textbf{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. \textbf{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, \textbf{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.

Problem

• executable data
• scalable synthesis
• software engineering agents
• long-horizon tasks
• reproducible environments

Innovation

• SWE-Hub
• executable software engineering tasks
• reproducible multi-language environments
• scalable bug synthesis
• repository-scale task generation