"待办事项：修复双子座模型造成的混乱"：理解生成式人工智能引发的自承技术债务

摘要

随着ChatGPT、Copilot、Claude和Gemini等大型语言模型（LLMs）逐渐融入软件开发工作流，开发者在其代码注释中留下的AI参与痕迹日益增多。其中部分注释不仅明确承认使用了生成式AI，还坦承存在技术缺陷。通过分析从公开的Python和JavaScript的GitHub代码库（2022年11月至2025年7月期间）中提取的6,540条涉及LLM的代码注释，我们识别出81条同时自承存在技术债务（SATD）的案例。开发者最常提及推迟测试、适配不完整以及对AI生成代码理解有限等问题，这表明AI辅助不仅影响技术债务产生的时间点，也改变了其形成原因。我们提出"生成式AI引发的自承技术债务"（GIST）这一概念框架，用以描述开发者在使用AI生成代码时，明确表达对其行为或正确性存在不确定性的反复出现的案例类型。

English

As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments explicitly acknowledge both the use of generative AI and the presence of technical shortcomings. Analyzing 6,540 LLM-referencing code comments from public Python and JavaScript-based GitHub repositories (November 2022-July 2025), we identified 81 that also self-admit technical debt(SATD). Developers most often describe postponed testing, incomplete adaptation, and limited understanding of AI-generated code, suggesting that AI assistance affects both when and why technical debt emerges. We term GenAI-Induced Self-admitted Technical debt (GIST) as a proposed conceptual lens to describe recurring cases where developers incorporate AI-generated code while explicitly expressing uncertainty about its behavior or correctness.

"待办事项：修复双子座模型造成的混乱"：理解生成式人工智能引发的自承技术债务

"TODO: Fix the Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical Debt

摘要

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