DevGPT: Studying Developer-ChatGPT Conversations
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Abstract (arXiv)
This paper introduces DevGPT, a dataset curated to explore how software developers interact with ChatGPT, a prominent large language model (LLM). The dataset encompasses 29,778 prompts and responses from ChatGPT, including 19,106 code snippets, and is linked to corresponding software development artifacts such as source code, commits, issues, pull requests, discussions, and Hacker News threads. This comprehensive dataset is derived from shared ChatGPT conversations collected from GitHub and Hacker News, providing a rich resource for understanding the dynamics of developer interactions with ChatGPT, the nature of their inquiries, and the impact of these interactions on their work. DevGPT enables the study of developer queries, the effectiveness of ChatGPT in code generation and problem solving, and the broader implications of AI-assisted programming. By providing this dataset, the paper paves the way for novel research avenues in software engineering, particularly in understanding and improving the use of LLMs like ChatGPT by developers.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Aug 2023.
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Citing papers per year: 2024: 2; 2025: 5; 2026: 5.
12 papers.
- 2024The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmerscited by 30
- 2025How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineeringcited by 15
- 2025ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulationcited by 11
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarkscited by 9
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shiftcited by 9
- 2025Do LLMs Consider Security? An Empirical Study on Responses to Programming Questionscited by 9
- 2026Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activitycited by 6
- 2026AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHubcited by 5
- 2025Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Codecited by 4
- 2026Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineeringcited by 1
- 2026"Nothing to See Here'': Unintended Disclosure through Revision Traces of LLM Deliverablescited by 0
- 2026Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Datacited by 0
- 2026Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data12 Oct 2026
- 2026"Nothing to See Here'': Unintended Disclosure through Revision Traces of LLM Deliverables28 Sep 2026
- 2026Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering18 Apr 2026
- 2026AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub4 Apr 2026
- 2026Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity26 Jan 2026
- 2025ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation26 Sep 2025
- 2025Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code18 Apr 2025
- 2025Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions20 Feb 2025
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shift12 Feb 2025
- 2025How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering15 Jan 2025
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarks15 Apr 2024
- 2024The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers3 Apr 2024