Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow
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Abstract (arXiv)
With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. GitHub Copilot, the AI pair programmer, utilizes machine learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. To this end, we conducted an empirical study to understand the problems that practitioners face when using Copilot, as well as their underlying causes and potential solutions. We collected data from 473 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts. Our results reveal that (1) Operation Issue and Compatibility Issue are the most common problems faced by Copilot users, (2) Copilot Internal Error, Network Connection Error, and Editor/IDE Compatibility Issue are identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify Configuration/Setting, and Use Suitable Version are the predominant solutions. Based on the results, we discuss the potential areas of Copilot for enhancement, and provide the implications for the Copilot users, the Copilot team, and researchers.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2023.
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Citing papers per year: 2025: 6; 2026: 4.
10 papers.
- 2025Large Language Models Miss the Multi-Agent Markcited by 25
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
- 2026Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomycited by 5
- 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistantscited by 4
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
- 2026Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmapcited by 3
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspectivecited by 2
- 2025On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practicescited by 1
- 2026From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountabilitycited by 0
- 2026Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systemscited by 0
- 2026Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systems1 Oct 2026
- 2026From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability10 May 2026
- 2026Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap6 May 2026
- 2026Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy5 Mar 2026
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective9 Nov 2025
- 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants17 Aug 2025
- 2025Large Language Models Miss the Multi-Agent Mark27 May 2025
- 2025On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices23 Apr 2025
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review10 Mar 2025
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Review8 Mar 2025