Git-Assistant: Planning-Based Support for Updating Git Repositories
For developers struggling with git, this hybrid AI assistant addresses the need for correct and safe repository management, though the improvement is incremental over existing LLM-based tools.
Git-Assistant combines LLMs with automated planning to help developers execute non-trivial git operations from natural language requests, achieving improved reliability and reduced errors compared to LLM-only approaches.
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.