SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913906448924672 |
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| author | Wang, Haoran Hou, Zhenyu Wei, Yao Tang, Jie Dong, Yuxiao |
| author_facet | Wang, Haoran Hou, Zhenyu Wei, Yao Tang, Jie Dong, Yuxiao |
| contents | Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE). Recent LLM-powered toolkits, such as OpenAI Codex and Cursor, have offered end-to-end automation of the software development process. However, building effective SWE agents remains challenging due to the lack of high-quality training data and effective test cases. To address this issue, we present SWE-Dev, an SWE agent built upon open-source LLMs. First, we develop a robust pipeline to synthesize test cases for patch evaluation. Second, we scale up agent trajectories to construct the training data for building SWE-Dev. Experiments on the SWE-bench-Verified benchmark show that the SWE-Dev models can achieve top performance among all open SWE agents. Specifically, the success rates of the SWE-Dev 7B and 32B parameter models reach 23.4% and 36.6%, respectively, outperforming state-of-the-art open-source models. All code, models, and datasets are publicly available at https://github.com/THUDM/SWE-Dev. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling Wang, Haoran Hou, Zhenyu Wei, Yao Tang, Jie Dong, Yuxiao Artificial Intelligence Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE). Recent LLM-powered toolkits, such as OpenAI Codex and Cursor, have offered end-to-end automation of the software development process. However, building effective SWE agents remains challenging due to the lack of high-quality training data and effective test cases. To address this issue, we present SWE-Dev, an SWE agent built upon open-source LLMs. First, we develop a robust pipeline to synthesize test cases for patch evaluation. Second, we scale up agent trajectories to construct the training data for building SWE-Dev. Experiments on the SWE-bench-Verified benchmark show that the SWE-Dev models can achieve top performance among all open SWE agents. Specifically, the success rates of the SWE-Dev 7B and 32B parameter models reach 23.4% and 36.6%, respectively, outperforming state-of-the-art open-source models. All code, models, and datasets are publicly available at https://github.com/THUDM/SWE-Dev. |
| title | SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.07636 |