"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support

• URL: http://arxiv.org/abs/2309.13684v3
• Date: Thu, 20 Feb 2025 20:54:03 GMT
• Title: "Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
• Authors: Jiachen Li, Elizabeth Mynatt, Varun Mishra, Jonathan Bell,
• Abstract summary: With the rise of generative AI, developers have started to adopt AI chatbots, such as ChatGPT, in their software development process.<n>We investigate and compare how developers integrate this assistance into their real-world coding experiences by conducting a thematic analysis of 1700+ Reddit posts.<n>Our findings suggest that ChatGPT offers fast, clear, comprehensive responses and fosters a more respectful environment than SO.
• Score: 10.028644951955886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software engineers have historically relied on human-powered Q&A platforms like Stack Overflow (SO) as coding aids. With the rise of generative AI, developers have started to adopt AI chatbots, such as ChatGPT, in their software development process. Recognizing the potential parallels between human-powered Q&A platforms and AI-powered question-based chatbots, we investigate and compare how developers integrate this assistance into their real-world coding experiences by conducting a thematic analysis of 1700+ Reddit posts. Through a comparative study of SO and ChatGPT, we identified each platform's strengths, use cases, and barriers. Our findings suggest that ChatGPT offers fast, clear, comprehensive responses and fosters a more respectful environment than SO. However, concerns about ChatGPT's reliability stem from its overly confident tone and the absence of validation mechanisms like SO's voting system. Based on these findings, we synthesized the design implications for future GenAI code assistants and recommend a workflow leveraging each platform's unique features to improve developer experiences.

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