[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83990-en":3,"doc-seo-83990-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83990,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","From Conversation to Contribution Characterizing Vibe Coding in Open Source Software","AI coding assistants like GitHub Copilot and Cursor have shifted from code suggestion to conversational collaboration, enabling vibe-coding workflows where developers steer AI outputs via natural-language dialogue. A study addresses limited understanding of how these chat interactions connect to later open-source development and teamwork. Researchers analyze 13,360 AI conversation sessions (79,172 user messages) across 1,356 OSS repositories and relate them to development histories, supplemented by a developer survey.","From Conversation to Contribution: Characterizing Vibe Coding in Open-Source Software  \nZihan Fang∗ , Yueke Zhang∗ , Ningzhi Tang†, Collin McMillan†, Toby Jia-Jun Li†, and Yu Huang∗  \n∗ Vanderbilt University, Nashville, TN, USA  \n†University of Notre Dame, Notre Dame, IN, USA  \narXiv :2607 .05677v 1 [ cs . SE] 6 Jul 2026  \nAbstract—AI coding assistants such as GitHub Copilot and Cursor have evolved from code-suggestion tools into conversational collaborators, enabling vibe-coding workflows in which developers guide AI-generated code through natural-language dialogue. Although researchers have increasingly recognized the importance of AI coding agents and begun examining their impact on open-source development, a comprehensive understanding of how developers’ chat-based interactions with AI relate to subsequent open-source development and collaboration remains limited. This hinders efforts to effectively design, evaluate, and govern AI-assisted open-source software development. To address this gap, we collected 13,360 AI conversation sessions comprising 79,172 user messages from 1,356 OSS repositories, linked them to repository development histories, and complemented this analysis with a targeted developer survey. We find heavier AI use in smaller, less mature, and less collaborative repositories. After AI adoption, projects tended to show more active contributors and lower contributor concentration (p \u003C .001), although communication remained highly concentrated. Code Writing was the dominant chat purpose, and nearly all AI chat sessions were followed by subsequent commits. We find no broad deterioration in code-quality signals or pull request merging rates. However, developers perceive others’ AI-generated code as harder to maintain than their own (p = .029) and view AI as lowering barriers to OSS contribution. While most (68%) are willing to share their chat, concerns remain around appearing incompetent, increasing reviewer burden, and exposing ideas to competitors. These findings provide a large-scale empirical characterization of AI-assisted OSS contribution and offer practical insights for designing and governing responsible vibe-coding practices in open-source development.  \nIndex Terms—AI Coding Assistants, Open Source Software, AI-assisted Programming  \nI. INTRODUCTION  \nAI coding assistants such as GitHub Copilot and Cursor have become increasingly common in modern software development [1], [2] . Compared with general-purpose browserbased chat interfaces such as ChatGPT, these tools operate directly within developers’ programming environments and can access local project context. They support multi-turn dialogue, coordinated multi-file edits, terminal interaction, and iterative refinement based on codebase and execution feedback. Developers can therefore express high-level intent in natural language and steer AI-generated changes through dialogue, using the assistant as a conversational collaborator within the development environment. This style of interaction is often described as vibe coding: an intent-driven workflow in which developers express goals through natural-language  \nprompts and rely on AI-generated code to varying degrees of manual oversight [3], [4] .  \nOpen-source software (OSS) provides a particularly relevant context for studying vibe coding workflows because many AIassisted development activities are observable in public repositories’ development histories, such as commits, issues, and pull requests (PRs) [5]–[7] . Understanding how developers make vibe-coding-based OSS contributions is important because OSS is a collaborative ecosystem, where AI-assisted coding may affect not only individual productivity but also contribution practices, maintenance work, and community trust. Recent industry reports suggest that AI-assisted development is already straining GitHub’s infrastructure: code changes were reportedly on pace to increase from about 1 billion in 2025 to 14 billion in 2026, prompting Microsoft ","cbCaijG6Bd3S3QoA","https://ap.wps.com/l/cbCaijG6Bd3S3QoA","pdf",1638866,4,1,12,"English","en",105,"# Introduction\n## Study Motivation\n# Data Collection and Method\n## Conversation Sessions and Repository Linking\n# Findings\n## Repository Characteristics and Dynamics\n## Code Purpose and Development Follow-through\n# Developer Perceptions and Implications","[{\"question\":\"What is vibe coding in the context of AI coding assistants?\",\"answer\":\"Vibe coding is an intent-driven workflow where developers express goals through natural-language prompts and guide AI-generated code with varying levels of manual oversight.\"},{\"question\":\"How did the study analyze the relationship between AI chat and OSS development?\",\"answer\":\"The study collected 13,360 AI conversation sessions (79,172 user messages) from 1,356 OSS repositories, linked each session to repository development histories, and added a targeted developer survey.\"},{\"question\":\"What effects did AI adoption have on OSS collaboration and development signals?\",\"answer\":\"After AI adoption, projects tended to gain more active contributors and show lower contributor concentration, and nearly all AI chat sessions were followed by subsequent commits, with no broad deterioration in code-quality or PR merging rates. Developers did report that others’ AI-generated code can be harder to maintain.\"}]",1784191889,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"from-conversation-to-contribution-characterizing-vibe-coding-in-open-source-software","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/from-conversation-to-contribution-characterizing-vibe-coding-in-open-source-software/83990/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is vibe coding in the context of AI coding assistants?","Question",{"text":75,"@type":76},"Vibe coding is an intent-driven workflow where developers express goals through natural-language prompts and guide AI-generated code with varying levels of manual oversight.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did the study analyze the relationship between AI chat and OSS development?",{"text":80,"@type":76},"The study collected 13,360 AI conversation sessions (79,172 user messages) from 1,356 OSS repositories, linked each session to repository development histories, and added a targeted developer survey.",{"name":82,"@type":73,"acceptedAnswer":83},"What effects did AI adoption have on OSS collaboration and development signals?",{"text":84,"@type":76},"After AI adoption, projects tended to gain more active contributors and show lower contributor concentration, and nearly all AI chat sessions were followed by subsequent commits, with no broad deterioration in code-quality or PR merging rates. 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