[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82261-en":3,"doc-seo-82261-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82261,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Git-Assistant: Planning-Based Support for Updating Git Repositories","Version control is critical for collaborative software development, but many practitioners struggle to perform correct git operations, especially for tasks like branching, merging, and conflict resolution. Git-Assistant combines large language models with automated planning to translate natural language requests into safe, actionable command sequences. The assistant analyzes repository context and uses formal reasoning to improve correctness. A systematic evaluation on synthetic and randomized git environments compares LLM-only versus planning-augmented variants across multiple metrics, showing higher reliability and fewer errors.","Git-Assistant: Planning-Based Support for Updating Git Repositories  \nAlfredo Garrachn Ruiz , Toms de la Rosa , Daniel Borrajo AI Research, JPMorganChase  \narXiv :2607 .09224v 1 [ cs . SE] 10 Jul 2026  \nAbstract  \nVersion 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.  \n1 Introduction  \nVersion control systems are fundamental to modern software development, with git standing out as the most widely adopted tool for managing collaborative workflows, code integration, and project history. However, despite its central role, git is often perceived as difficult to master. Both new and experienced developers encounter significant challenges, especially when dealing with complex operations such as branching, merging, and resolving conflicts [Perez De Rosso and Jackson, 2013] . Empirical studies and analyses of developer activity on platforms like GitHub and Stack Overflow [Yang et al., 2022; Milliken et al., 2021] consistently show that misunderstandings and mistakes are common in everyday git usage. Developers frequently make errors in repository management and pull request workflows, highlighting the cognitive demands and error-prone nature of manual git operations [Kinsman et al., 2021] . These difficulties are  \nfurther compounded by git’s complex command-line interface and distributed model, which require users to memorize sequences of commands and understand subtle repository states [De Rosso and Jackson, 2016] . Modern git clientsand IDE plugins offer some support through the git graph (i.e., visual representations of repository history) and simplified menus of git commands, but they often fall short when it comes to constructing the full sequence of commands needed to achieve a specific repository state.  \nRecent advances in Large Language Models (LLMs) have demonstrated that these models are highly effective at understanding and generating natural language [Minaee et al., 2024] . This makes LLMs a promising foundation for building AI-based assistants that can interpret developer intent and provide contextual guidance for git operations. By bridging the gap between natural language requests and precise git command sequences, LLMs have the potential to lower the barrier to effective git usage and reduce the likelihood of mistakes.  \nNevertheless, while LLMs are good at language understanding, the complexity of repository management tasks suggests that additional formal reasoning techniques, such as automated planning, may be needed to ensure the correctness and safety of generated command sequences. There is a growing need for intelligent, context-aware assistants that can analyze the current repository state, recommend operations, and help prevent users from reaching undesirable or inconsistent git states.  \nIn this work, we present Git-Assistant, a CLI assistant that receives developers’ git-related requests and provides a sequence of git commands to address those requests. 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problem does Git-Assistant address in git repository updates?","Question",{"text":74,"@type":75},"Git-Assistant targets the difficulty and error-proneness of preparing correct git command sequences, particularly for non-trivial operations that require understanding repository state and avoiding inconsistent outcomes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does Git-Assistant improve command generation accuracy and safety?",{"text":79,"@type":75},"It integrates automated planning with LLMs: the assistant analyzes repository context, converts natural language requests into command sequences, and uses formal reasoning to enhance correctness and reduce unsafe results.",{"name":81,"@type":72,"acceptedAnswer":82},"How is Git-Assistant evaluated in the paper?",{"text":83,"@type":75},"The paper proposes a systematic evaluation using synthetic and randomized git environments, comparing LLM-only and planning-augmented versions across multiple metrics to measure reliability and 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