Faster Code, Deeper Debt? A Multivocal Literature Review on Technical Debt and Its Early Signs in LLM-Assisted Software Development

• URL: http://arxiv.org/abs/2606.14796v1
• Date: Thu, 11 Jun 2026 14:44:30 GMT
• Title: Faster Code, Deeper Debt? A Multivocal Literature Review on Technical Debt and Its Early Signs in LLM-Assisted Software Development
• Abstract summary: We conduct a multivocal review of 104 sources to examine how LLM-assisted development contributes to technical debt.<n>We identify fast-integration debt, where rapidly generated code speed over quality, triggering a domino effect that leads to governance debt and increased long-term maintenance costs.<n> strategies suggested in the literature include human-in-the-loop frameworks, prompt engineering, and data quality alignment.
• Score: 7.433830772352091
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid adoption of LLM-assisted coding, the need to manage the technical debt these systems introduce has become urgent. In this paper, we conduct a multivocal literature review of 104 sources (31 formal, 73 grey) to examine how LLM-assisted development contributes to technical debt and what strategies, metrics, and benchmarks exist to mitigate it. We find that LLMs often amplify traditional forms of technical debt, particularly code, design, and documentation debts, while also introducing new LLM-specific debts. Notably, we identify fast-integration debt, where rapidly generated code prioritizes speed over quality, triggering a domino effect that leads to governance debt and increased long-term maintenance costs. Additional emerging categories include prompt, ethical, data, and provenance debt, reflecting new challenges unique to LLM adoption. To address these, strategies suggested in the literature include human-in-the-loop frameworks, prompt engineering, and data quality alignment. In practice, tools such as SonarQube are commonly used to detect technical debt indicators, while research prototypes such as CodeSmellEval are emerging to assess how LLMs contribute to debts. However, no standardized benchmarks or LLM-specific metrics yet exist, leaving an important gap. Based on findings, we outline insights and future directions to ensure reliable integration of LLMs into software engineering workflows.

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