# How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering

arXiv: https://arxiv.org/abs/2501.08774 · HTML: https://mlsanity.com/paper/2501.08774/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Greening Large Language Models of Code](https://mlsanity.com/paper/2309.04076/) (2023)
- [From Copilot to Pilot: Towards AI Supported Software Development](https://mlsanity.com/paper/2303.04142/) (2023)
- [DevGPT: Studying Developer-ChatGPT Conversations](https://mlsanity.com/paper/2309.03914/) (2023)
- [Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions](https://mlsanity.com/paper/2402.08967/) (2024)
- [Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot](https://mlsanity.com/paper/2309.05687/) (2023)
- [AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations](https://mlsanity.com/paper/2408.05344/) (2024)

## Built on by (most cited first, 15 of 15)

- [Generative AI and Empirical Software Engineering: A Paradigm Shift](https://mlsanity.com/paper/2502.08108/) (2025)
- [Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap](https://mlsanity.com/paper/2605.04532/) (2026)
- [Humans are Missing from AI Coding Agent Research](https://mlsanity.com/paper/2608.12355/) (2026)
- [Steering LLMs via Scalable Interactive Oversight](https://mlsanity.com/paper/2602.04210/) (2026)
- [Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles](https://mlsanity.com/paper/2605.08017/) (2026)
- [User Misconceptions of LLM-Based Conversational Programming Assistants](https://mlsanity.com/paper/2510.25662/) (2025)
- [Comparing Developer and LLM Biases in Code Evaluation](https://mlsanity.com/paper/2603.24586/) (2026)
- [Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering](https://mlsanity.com/paper/2604.10530/) (2026)
- [Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks](https://mlsanity.com/paper/2508.04125/) (2025)
- [CLINB: A Climate Intelligence Benchmark for Foundational Models](https://mlsanity.com/paper/2511.11597/) (2025)
- [AI-driven Software Development: A Pragmatic Path to Agentic Development Processes](https://mlsanity.com/paper/2606.15283/) (2026)
- [Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaboration](https://mlsanity.com/paper/2608.11460/) (2026)
- [Between the Commits: Process, Error, and Claim Reliability in a Wholly AI-Authored Codebase](https://mlsanity.com/paper/2609.29744/) (2026)
- [A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code](https://mlsanity.com/paper/2609.30642/) (2026)
- [PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models](https://mlsanity.com/paper/2610.16980/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.