发表机构
AI Research, JPMorganChase（人工智能研究，摩根大通）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对git工具对开发者有挑战的问题，提出结合大语言模型与自动规划的Git-Assistant，经合成和随机git环境评估，证明该方法能提升仓库管理可靠性、减少错误，展现混合人工智能方法在开发者辅助上的潜力。
AI 中文摘要
版本控制系统对协作软件开发至关重要，但像git这样的工具对许多从业者来说仍具挑战性。大语言模型（LLMs）虽能解读开发者意图，但在仓库管理任务中的有效性受形式推理需求限制。本文介绍了Git-Assistant，一种将LLMs与自动规划相结合的基于人工智能的助手，以支持开发者执行复杂的git操作。助手分析仓库上下文，将自然语言请求转换为可操作的命令序列，并运用规划技术确保正确性和安全性。通过合成和随机的git环境进行系统评估，比较了仅使用LLMs和规划增强变体在多个指标上的性能。实验结果表明，将形式推理与LLMs集成可提高仓库管理的可靠性并减少错误，凸显了混合人工智能方法在智能开发者辅助方面的潜力。
英文摘要
Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners. Recent advances in Large Language Models (LLMs) offer promising capabilities for interpreting developer intent, but their effectiveness in repository management tasks is limited by the need for formal reasoning. This work introduces Git-Assistant, an AI-based assistant that combines LLMs with automated planning to support developers in executing non-trivial git operations. The assistant analyzes repository context, translates natural language requests into actionable command sequences, and incorporates planning techniques to ensure correctness and safety. We present a systematic evaluation methodology using synthetic and randomized git environments, comparing the performance of LLM-only and planning-augmented variants across multiple metrics. Experimental results demonstrate that integrating formal reasoning with LLMs improves reliability and reduces errors in repository management, highlighting the potential of hybrid AI approaches for intelligent developer assistance.
Comments11 pages, 6 tables, 3 figures
