mlsanityData to 14 Oct 2026

Grounded Copilot: How Programmers Interact with Code-Generating Models

arXiv 2206.15000 ↗First posted 30 Jun 2022cs.HC cs.PLcited by 218

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Cited by 41 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants--with a range of prior experience using the assistant--as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.

Key facts. Cited by 218 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jun 2022.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 9; 2023: 30; 2024: 59; 2025: 46; 2026: 74.

218 papers.

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  1. 2024A Survey on Large Language Models for Code Generationcited by 658
  2. 2023Using an LLM to Help With Code Understandingcited by 228
  3. 2022Multi-lingual Evaluation of Code Generation Modelscited by 159
  4. 2022Large Language Models Meet NL2Code: A Surveycited by 138
  5. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 93
  6. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  7. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  8. 2022Natural Language to Code Generation in Interactive Data Science Notebookscited by 87
  9. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 86
  10. 2024Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Surveycited by 83
  11. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 82
  12. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
38 more
  1. 2024Building Machines that Learn and Think with Peoplecited by 73
  2. 2022CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Contextcited by 65
  3. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 64
  4. 2024PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinementcited by 62
  5. 2023Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repaircited by 55
  6. 2023Computing Education in the Era of Generative AIcited by 52
  7. 2024Large Language Model for Table Processing: A Surveycited by 52
  8. 2024Calibration and Correctness of Language Models for Codecited by 49
  9. 2024A Performance Study of LLM-Generated Code on Leetcodecited by 49
  10. 2025RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditingcited by 49
  11. 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 48
  12. 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 45
  13. 2024CodeJudge: Evaluating Code Generation with Large Language Modelscited by 44
  14. 2023Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questionscited by 42
  15. 2024CYCLE: Learning to Self-Refine the Code Generationcited by 42
  16. 2023RepoFusion: Training Code Models to Understand Your Repositorycited by 40
  17. 2024Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completioncited by 40
  18. 2022What is it like to program with artificial intelligence?cited by 39
  19. 2026Dive into Claude Code: The Design Space of Today's and Future AI Agent Systemscited by 38
  20. 2024Automatic Programming: Large Language Models and Beyondcited by 36
  21. 2025Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Supportcited by 34
  22. 2023StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Codecited by 32
  23. 2023Data Formulator: AI-powered Concept-driven Visualization Authoringcited by 32
  24. 2024REPOFUSE: Repository-Level Code Completion with Fused Dual Contextcited by 32
  25. 2026Agentic Much? Adoption of Coding Agents on GitHubcited by 32
  26. 2023Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?cited by 31
  27. 2023Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Promptscited by 30
  28. 2024The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmerscited by 30
  29. 2024Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmapcited by 30
  30. 2024Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decompositioncited by 29
  31. 2022Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writingcited by 27
  32. 2024Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seekingcited by 26
  33. 2023When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programmingcited by 25
  34. 2023BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Modelscited by 25
  35. 2024Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practicecited by 25
  36. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 25
  37. 2024Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chatcited by 23
  38. 2023AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluationcited by 21

50 most cited of 218 All 218 (JSON)