Abstract
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.
Recommended Citation
A. Al Mujahid and M. M. Imran, ""TODO: Fix The Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical Debt," Proceedings 2026 ACM IEEE International Conference on Technical Debt Techdebt 2026, pp. 1 - 6, Association for Computing Machinery, Jul 2026.
The definitive version is available at https://doi.org/10.1145/3794915.3795777
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
AI-assisted software development; generative AI; human-AI collaboration; large language models; self-admitted technical debt
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 The Author(s), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
16 Jul 2026
