Problem
Research questions and friction points this paper is trying to address.
Git
Large Language Models
Automated Planning
Repository Management
Developer Assistance
Git-Assistant: Planning-Based Support for Updating Git Repositories
🤖 AI Summary
This work addresses the challenge that developers often make errors when performing complex Git operations, and existing large language models (LLMs) struggle to ensure correctness and safety due to their limited formal reasoning capabilities. To overcome this limitation, the paper proposes a novel approach that integrates automated planning with LLMs, enabling the system to interpret natural language instructions and formally model the state of a Git repository to generate safe, verifiable command sequences. By combining symbolic reasoning with language understanding, the method significantly improves the success rate of Git operations compared to pure LLM-based solutions. Experimental results demonstrate consistent performance gains across multiple evaluation metrics, thereby enhancing both the reliability and interpretability of AI-powered developer assistance tools.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Automated Planning
Git Operations
Formal Reasoning
Developer Assistance
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
