基于开发流程的开源模型，显著提升自动软件修复能力。
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
- 从真实代码提交过程学习，融合开发动态与迭代思维
- 72B版解决30.20%的GitHub问题，较Llama 3.1提升22.76%
- 小模型7B版也达18.20%效果，适合资源受限场景
近年来大模型代理在自动化软件工程方面取得进展，尤其在维护与演化任务中。但现有研究面临两大挑战：一是顶尖性能依赖闭源模型，限制可及性与定制化；二是模型主要训练于静态代码数据，缺乏对开发过程中动态交互、迭代求解和演化特性的理解。为此，本文从软件工程视角出发，认为真实开发不仅包含代码，还涵盖开发者思维、工具使用及人员协作。因此提出Lingma SWE-GPT系列（7B与72B版本），通过学习真实代码提交行为，系统建模开发中的动态交互与迭代过程，实现对软件改进全流程的深入理解。在SWE-bench Verified基准测试中，Lingma SWE-GPT 72B成功解决30.20%的GitHub问题，相比Llama 3.1 405B有22.76%相对提升，接近GPT-4o的31.80%表现。7B版本解决18.20%问题，证明小型模型在自动化软件工程任务中的潜力。
原文摘要 · Abstract (English)
Recent advancements in LLM-based agents have led to significant progress in automatic software engineering, particularly in software maintenance and evolution. Despite these encouraging advances, current research faces two major challenges. First, SOTA performance primarily depends on closed-source models, which significantly limits the technology's accessibility, and potential for customization in diverse SE tasks. Second, these models are predominantly trained on static code data, lacking a deep understanding of the dynamic interactions, iterative problem-solving processes, and evolutionary characteristics inherent in software development. To address these challenges, our study adopts a software engineering perspective. We recognize that real-world software maintenance and evolution processes encompass not only static code data but also developers' thought processes, utilization of external tools, and the interaction between different functional personnel. Consequently, we introduce the Lingma SWE-GPT series, comprising Lingma SWE-GPT 7B and 72B. By learning from and simulating real-world code submission activities, Lingma SWE-GPT systematically incorporates the dynamic interactions and iterative problem-solving inherent in software development process, thereby achieving a more comprehensive understanding of software improvement processes. We conducted experimental evaluations using SWE-bench Verified benchmark. The results demonstrate that Lingma SWE-GPT 72B successfully resolves 30.20% of the GitHub issues, marking a significant improvement in automatic issue resolution (22.76% relative improvement compared to Llama 3.1 405B), approaching the performance of closed-source models (31.80\% issues of GPT-4o resolved). Notably, Lingma SWE-GPT 7B resolves 18.20% of the issues, highlighting the potential for applying smaller models to ASE tasks.
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