[Paper] DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch

Source: arXiv - 2606.10728v1

Overview

As the capabilities of LLM-based code agents continue to advance, their expected role is expanding beyond localized bug fixing in existing codebases toward architecting and implementing complete software repositories from high-level specifications. However, training agents for such long-horizon software engineering tasks remains difficult due to the scarcity of large-scale, verifiable whole-repository generation data. In this paper, we introduce \textbf{DeNovoSWE}, a large-scale dataset for whole-repository generation. DeNovoSWE comprises 4,818 high-quality instances, where each instance requires generating a complete repository from documentation. Our dataset is automatically constructed through a carefully designed sandboxed agentic workflow, enabling scalable curation without human annotation. DeNovoSWE is constructed with “divide and conquer” and critic-repair philosophy. To balance data quality and diversity, we further introduce a difficulty-aware trajectory filtering strategy. Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.

Key Contributions

This paper presents research in the following areas:

• cs.SE

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.SE.

Authors

• Jiale Zhao
• Guoxin Chen
• Fanzhe Meng
• Wayne Xin Zhao
• Ruihua Song
• Ji-Rong Wen
• Kai Jia

Paper Information

• arXiv ID: 2606.10728v1
• Categories: cs.SE
• Published: June 9, 2026
• PDF: Download PDF