"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools
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Abstract (arXiv)
While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online communities shape developers' trust in AI tools and how we can leverage community features to facilitate appropriate user trust. Through interviewing 17 developers, we find that developers collectively make sense of AI tools using the experiences shared by community members and leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of using community features to support user trust in AI code generation systems. We synthesize our findings and extend an existing model of user trust in AI technologies with sociotechnical factors. We map out the design considerations for integrating user community into the AI code generation experience.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2022.
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Citing papers per year: 2023: 3; 2024: 4; 2025: 4; 2026: 3.
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2 more
- 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
- 2024AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracycited by 21
- 2023Toward General Design Principles for Generative AI Applicationscited by 20
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 10
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
- 2023Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippetscited by 3
- 2024AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionalscited by 3
- 2025Large Language Models for Software Testing: A Research Roadmapcited by 2
- 2026Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomescited by 2
- 2024Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoringcited by 1
- 2025User Misconceptions of LLM-Based Conversational Programming Assistantscited by 1
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needscited by 1
12 most cited of 13 All 13 (JSON)
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needs20 Feb 2026
- 2026Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes9 Feb 2026
- 2025User Misconceptions of LLM-Based Conversational Programming Assistants29 Oct 2025
- 2025Large Language Models for Software Testing: A Research Roadmap29 Sep 2025
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives18 Mar 2025
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Review8 Mar 2025
- 2024Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring20 Dec 2024
- 2024AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals19 Dec 2024
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations12 Jul 2024
- 2024AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy12 Feb 2024
- 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges30 Mar 2023
- 2023Toward General Design Principles for Generative AI Applications13 Jan 2023
12 newest of 13 All 13 (JSON)