Paper deep dive
Git-Assistant: Planning-Based Support for Updating Git Repositories
Alfredo Garrachón Ruiz, Tomás de la Rosa, Daniel Borrajo
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 94%
Last extracted: 7/18/2026, 10:38:57 AM
Summary
The paper 'Git-Assistant: Planning-Based Support for Updating Git Repositories' proposes an AI-based assistant that combines Large Language Models (LLMs) with automated planning to assist developers in executing complex git operations. It aims to translate natural language requests into correct and safe command sequences by integrating formal reasoning with LLMs, addressing the limitations of LLM-only approaches in repository management. The work includes a systematic evaluation using synthetic environments, demonstrating improved reliability and error reduction.
Entities (8)
Relation Signals (8)
Alfredo Garrachón Ruiz → authored → Git-Assistant
confidence 98% · Authors: Alfredo Garrachón Ruiz... Title: Git-Assistant...
Tomás de la Rosa → authored → Git-Assistant
confidence 98% · Authors: ... Tomás de la Rosa ... Title: Git-Assistant...
Daniel Borrajo → authored → Git-Assistant
confidence 98% · Authors: ... Daniel Borrajo ... Title: Git-Assistant...
Git-Assistant → uses → Large Language Models
confidence 95% · This work introduces Git-Assistant, an AI-based assistant that combines LLMs with automated planning
Git-Assistant → uses → Automated Planning
confidence 95% · combines LLMs with automated planning to support developers in executing non-trivial git operations
Git-Assistant → supports → git operations
confidence 90% · support developers in executing non-trivial git operations
Git-Assistant → improves → Reliability
confidence 85% · integrating formal reasoning with LLMs improves reliability
Git-Assistant → reduces → errors
confidence 85% · reduces errors in repository management
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Abstract
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.
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- Source: https://arxiv.org/abs/2607.09224v2
- Canonical: https://arxiv.org/abs/2607.09224v2
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Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2607.09224v2 (cs) This paper has been withdrawn by Alfredo Garrachón Ruiz [Submitted on 10 Jul 2026 (v1), last revised 14 Jul 2026 (this version, v2)] Title:Git-Assistant: Planning-Based Support for Updating Git Repositories Authors:Alfredo Garrachón Ruiz, Tomás de la Rosa, Daniel Borrajo View a PDF of the paper titled Git-Assistant: Planning-Based Support for Updating Git Repositories, by Alfredo Garrach\'on Ruiz and 2 other authors No PDF available, click to view other formats 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. Comments: Pending permissions from the private company Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2607.09224 [cs.SE] (or arXiv:2607.09224v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.09224 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alfredo Garrachón Ruiz [view email] [v1] Fri, 10 Jul 2026 09:16:20 UTC (277 KB) [v2] Tue, 14 Jul 2026 10:25:32 UTC (1 KB) (withdrawn) Full-text links: Access Paper: View a PDF of the paper titled Git-Assistant: Planning-Based Support for Updating Git Repositories, by Alfredo Garrach\'on Ruiz and 2 other authorsWithdrawn No license for this version due to withdrawn Current browse context: cs.SE < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from