SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories

• URL: http://arxiv.org/abs/2509.08724v1
• Date: Wed, 10 Sep 2025 16:15:23 GMT
• Title: SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories
• Authors: Junhao Wang, Daoguang Zan, Shulin Xin, Siyao Liu, Yurong Wu, Kai Shen,
• Abstract summary: SWE-Mirror is a pipeline that distills a real-world issue's semantic essence, mirrors it into another repository with a configured Gym environment, and re-animates it as a verifiable issue-resolving task.<n>Applying SWE-Mirror to 40 repositories across 4 languages, we have curated a dataset with 60,671 issue-resolving tasks.<n>Post-training experiments show that models trained with the dataset exhibit improvements in issue-resolving capabilities.
• Score: 15.458389392000706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating large-scale verifiable training datasets for issue-resolving tasks is a critical yet notoriously difficult challenge. Existing methods on automating the Gym environment setup process for real-world issues suffer from low success rates and high overhead. Meanwhile, synthesizing new tasks within existing Gym environments leaves the vast pool of authentic, human-reported problems untapped. To maximize the utilization of existing Gym environments and also the rich data of issue-resolving history on GitHub, we introduce SWE-Mirror, a pipeline that distills a real-world issue's semantic essence, mirrors it into another repository with a configured Gym environment, and re-animates it as a verifiable issue-resolving task. SWE-Mirror reuses existing Gym environments along with the vast pool of issue-resolving history hosted on GitHub to construct a large-scale dataset of mirrored authentic and verifiable tasks. Applying SWE-Mirror to 40 repositories across 4 languages, we have curated a dataset with 60,671 issue-resolving tasks and demonstrated the value of our dataset by training and evaluating coding agents at various scale. Post-training experiments show that models trained with the dataset exhibit improvements in issue-resolving capabilities. Furthermore, by extending the dataset size to over 12,000 high-quality trajectories, we established a new state-of-the-art (SOTA) among Qwen2.5-Coder-Instruct based LLMs on the OpenHands agent framework, which increases the resolve rate on SWE-Bench-Verified by +21.8% for the 7B model and +46.0% for the 32B model and validates the effectiveness of our approach.

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